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ORCIDTong Zhang 0001
张潼
Person information
- unicode name: 张潼
- affiliation: University of Illinois Urbana-Champaign, IL, USA
- affiliation (former): Hong Kong University of Science and Technology, China
- affiliation (former): Tencent AI Lab, Shenzhen, China
- affiliation (former): Rutgers University, Department of Statistics, NJ, USA
- affiliation (former): Baidu Inc. Beijing, China
- affiliation (former): Yahoo
- affiliation (former): IBM T. J. Watson Research Center, Yorktown Heights, NY, USA
- affiliation (PhD): Stanford University, CA, USA
Other persons with the same name
- Tong Zhang — disambiguation page
- Tong Zhang 0002
![[0009-0009-8005-0043]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0009-0009-8005-0043")
— Rensselaer Polytechnic Institute, Troy, NY, USA (and 1 more) - Tong Zhang 0003 — Shanghai Jiaotong University, Department of Computer Science and Technology, China
- Tong Zhang 0004 — University of Southern California, Department of Electrical Engineering Systems, Los Angeles, CA, USA
- Tong Zhang 0005 — University of Illinois, Beckman Institute, Urbana, IL, USA
- Tong Zhang 0006 — SRA Corporation, Arlington, VA, USA
- Tong Zhang 0007 — Hewlett-Packard Labs, Palo Alto, CA, USA
- Tong Zhang 0008 — National University of Defense Technology, Changsha, China
- Tong Zhang 0009 — Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, China (and 2 more)
- Tong Zhang 0010 — Henan Polytechnic University, School of Mathematics and Information Science, Jiaozuo, China (and 1 more)
- Tong Zhang 0011 — Wuhan University of Technology, Intelligent Transportation Systems Research Center, China
- Tong Zhang 0012 — Tsinghua University, Department of Engineering Mechanics, School of Aerospace, Beijing, China
- Tong Zhang 0013 — Kyushu University, Graduate School of Information Science and Electrical Engineering, Fukuoka, Japan
- Tong Zhang 0014 — Southeast University, School of Electronic Science and Engineering, MOE Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Nanjing, China
- Tong Zhang 0015 — South China University of Technology, School of Electronics and Information, Guangzhou, China (and 1 more)
- Tong Zhang 0016 — Fuzhou University, College of Mathematics & Computer Science, China
- Tong Zhang 0017 — Peng Cheng Laboratory, Shenzhen, China (and 2 more)
- Tong Zhang 0018 — Tsinghua University, Department of Computer Science and Technology, TNList, Beijing, China
- Tong Zhang 0019 — University of Washington, Department of Electrical Engineering, Seattle, WA, USA
- Tong Zhang 0020 — University of Edinburgh, Institute for Digital Communications, Edinburgh, UK
- Tong Zhang 0021 — Nanjing University of Science and Technology, School of Computer Science and Technology, China (and 1 more)
- Tong Zhang 0023 — EPFL, Lausanne, Switzerland (and 1 more)
- Tong Zhang 0024 — Peking University, Institute of Computer Science and Technology, Beijing, China
- Tong Zhang 0025 — University of Windsor, ON, Canada
- Tong Zhang 0026 — Southern University of Science and Technology, Department of Electrical and Electronic Engineering, Shenzhen, China (and 1 more)
- Tong Zhang 0027 — Jilin University, College of Computer Science and Technology, Changchun, China
- Tong Zhang 0028 — Beijing Institute of Technology, Beijing Key Laboratory of Embedded Real-Time Information Processing Technology, School of Information and Electronics, China
- Tong Zhang 0029 — Southern University of Science and Technology, Department of Computer Science and Engineering, Shenzhen, China
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2020 – today
- 2024
[j84]Binnie Wai-Keung Yiu, Tong Zhang, Cheuk-Wing Lee:
Short-Term Load Forecasting Using Regularized Greedy Forest-Based Ensemble Model. IEEE Access 12: 112426-112439 (2024)- [j83]Shixiang Chen, Shiqian Ma, Anthony Man-Cho So, Tong Zhang:
Nonsmooth Optimization over the Stiefel Manifold and Beyond: Proximal Gradient Method and Recent Variants. SIAM Rev. 66(2): 319-352 (2024) - [j82]Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, Tong Zhang:
On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data. Trans. Mach. Learn. Res. 2024 (2024) - [c248]Shizhe Diao, Pengcheng Wang, Yong Lin, Rui Pan, Xiang Liu, Tong Zhang:
Active Prompting with Chain-of-Thought for Large Language Models. ACL (1) 2024: 1330-1350 - [c247]Haoxiang Wang, Yong Lin, Wei Xiong, Rui Yang, Shizhe Diao, Shuang Qiu, Han Zhao, Tong Zhang:
Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards. ACL (1) 2024: 8642-8655 - [c246]Cheng Niu, Xingguang Wang, Xuxin Cheng, Juntong Song, Tong Zhang:
Enhancing Dialogue State Tracking Models through LLM-backed User-Agents Simulation. ACL (1) 2024: 8724-8741 - [c245]Cheng Niu, Yuanhao Wu, Juno Zhu, Siliang Xu, Kashun Shum, Randy Zhong, Juntong Song, Tong Zhang:
RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models. ACL (1) 2024: 10862-10878 - [c244]Xunpeng Huang, Difan Zou, Hanze Dong, Yi-An Ma, Tong Zhang:
Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo. COLT 2024: 2438-2493 - [c243]Jiaqi Tang, Hao Lu, Xiaogang Xu, Ruizheng Wu, Sixing Hu, Tong Zhang, Tsz Wa Cheng, Ming Ge, Ying-Cong Chen, Fugee Tsung:
An Incremental Unified Framework for Small Defect Inspection. ECCV (31) 2024: 307-324 - [c242]Renjie Pi, Tianyang Han, Wei Xiong, Jipeng Zhang, Runtao Liu, Rui Pan, Tong Zhang:
Strengthening Multimodal Large Language Model with Bootstrapped Preference Optimization. ECCV (33) 2024: 382-398 - [c241]Dimitris Stripelis, Zhaozhuo Xu, Zijian Hu, Alay Dilipbhai Shah, Han Jin, Yuhang Yao, Jipeng Zhang, Tong Zhang, Salman Avestimehr, Chaoyang He:
TensorOpera Router: A Multi-Model Router for Efficient LLM Inference. EMNLP (Industry Track) 2024: 452-462 - [c240]Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan Yao, Tong Zhang:
Mitigating the Alignment Tax of RLHF. EMNLP 2024: 580-606 - [c239]Haoxiang Wang, Wei Xiong, Tengyang Xie, Han Zhao, Tong Zhang:
Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts. EMNLP (Findings) 2024: 10582-10592 - [c238]Renjie Pi, Tianyang Han, Jianshu Zhang, Yueqi Xie, Rui Pan, Qing Lian, Hanze Dong, Jipeng Zhang, Tong Zhang:
MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance. EMNLP 2024: 16012-16027 - [c237]Rui Yang, Han Zhong, Jiawei Xu, Amy Zhang, Chongjie Zhang, Lei Han, Tong Zhang:
Towards Robust Offline Reinforcement Learning under Diverse Data Corruption. ICLR 2024 - [c236]Xunpeng Huang, Hanze Dong, Yifan Hao, Yian Ma, Tong Zhang:
Reverse Diffusion Monte Carlo. ICLR 2024 - [c235]Yong Lin, Lu Tan, Yifan Hao, Honam Wong, Hanze Dong, Weizhong Zhang, Yujiu Yang, Tong Zhang:
Spurious Feature Diversification Improves Out-of-distribution Generalization. ICLR 2024 - [c234]Rui Pan, Yuxing Liu, Xiaoyu Wang, Tong Zhang:
Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise. ICLR 2024 - [c233]Wei Xiong, Hanze Dong, Chenlu Ye, Ziqi Wang, Han Zhong, Heng Ji, Nan Jiang, Tong Zhang:
Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint. ICML 2024 - [c232]Alekh Agarwal, Jian Qian, Alexander Rakhlin, Tong Zhang:
The Non-linear F-Design and Applications to Interactive Learning. ICML 2024 - [c231]Xunpeng Huang, Difan Zou, Hanze Dong, Yian Ma, Tong Zhang:
Faster Sampling via Stochastic Gradient Proximal Sampler. ICML 2024 - [c230]Chenlu Ye, Jiafan He, Quanquan Gu, Tong Zhang:
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption. ICML 2024 - [c229]Dake Zhang, Boxiang Lyu, Shuang Qiu, Mladen Kolar, Tong Zhang:
Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning. ICML 2024 - [c228]Helbert Paat, Qing Lian, Weilong Yao, Tong Zhang:
MEDL-U: Uncertainty-aware 3D Automatic Annotation based on Evidential Deep Learning. ICRA 2024: 13976-13982 - [c227]Rui Xie, Linsen Ma, Alex Zhong, Feng Chen, Tong Zhang:
ZipCache: A DRAM/SSD Cache with Built-in Transparent Compression. MEMSYS 2024: 116-128 - [c226]Shizhe Diao, Rui Pan, Hanze Dong, Kashun Shum, Jipeng Zhang, Wei Xiong, Tong Zhang:
LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. NAACL (Demonstrations) 2024: 116-127 - [c225]Zixuan Zhang, Revanth Gangi Reddy, Kevin Small, Tong Zhang, Heng Ji:
Towards Better Generalization in Open-Domain Question Answering by Mitigating Context Memorization. NAACL-HLT (Findings) 2024: 742-753 - [c224]Hanning Zhang, Shizhe Diao, Yong Lin, Yi R. Fung, Qing Lian, Xingyao Wang, Yangyi Chen, Heng Ji, Tong Zhang:
R-Tuning: Instructing Large Language Models to Say 'I Don't Know'. NAACL-HLT 2024: 7113-7139 - [c223]Ying Su, Jipeng Zhang, Yangqiu Song, Tong Zhang:
PipeNet: Question Answering with Semantic Pruning over Knowledge Graphs. *SEM@NAACL 2024: 360-371 - [i225]Yuanhao Wu, Juno Zhu, Siliang Xu, Kashun Shum, Cheng Niu, Randy Zhong, Juntong Song, Tong Zhang:
RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models. CoRR abs/2401.00396 (2024) - [i224]Ernest Perkowski, Rui Pan, Tuan Dung Nguyen, Yuan-Sen Ting, Sandor Kruk, Tong Zhang, Charlie O'Neill, Maja Jablonska, Zechang Sun, Michael J. Smith, Huiling Liu, Kevin Schawinski, Kartheik Iyer, Ioana Ciuca, UniverseTBD:
AstroLLaMA-Chat: Scaling AstroLLaMA with Conversational and Diverse Datasets. CoRR abs/2401.01916 (2024) - [i223]Renjie Pi, Tianyang Han, Yueqi Xie, Rui Pan, Qing Lian, Hanze Dong, Jipeng Zhang, Tong Zhang:
MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance. CoRR abs/2401.02906 (2024) - [i222]Xunpeng Huang, Difan Zou, Hanze Dong, Yian Ma, Tong Zhang:
Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo. CoRR abs/2401.06325 (2024) - [i221]Yifan Hao, Tong Zhang:
The Surprising Harmfulness of Benign Overfitting for Adversarial Robustness. CoRR abs/2401.12236 (2024) - [i220]Ying Su, Jipeng Zhang, Yangqiu Song, Tong Zhang:
PipeNet: Question Answering with Semantic Pruning over Knowledge Graphs. CoRR abs/2401.17536 (2024) - [i219]Tianyang Han, Qing Lian, Rui Pan, Renjie Pi, Jipeng Zhang, Shizhe Diao, Yong Lin, Tong Zhang:
The Instinctive Bias: Spurious Images lead to Hallucination in MLLMs. CoRR abs/2402.03757 (2024) - [i218]Chenlu Ye, Wei Xiong, Yuheng Zhang, Nan Jiang, Tong Zhang:
A Theoretical Analysis of Nash Learning from Human Feedback under General KL-Regularized Preference. CoRR abs/2402.07314 (2024) - [i217]Chenlu Ye, Jiafan He, Quanquan Gu, Tong Zhang:
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption. CoRR abs/2402.08991 (2024) - [i216]Ying Su, Tianqing Fang, Huiru Xiao, Weiqi Wang, Yangqiu Song, Tong Zhang, Lei Chen:
EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion. CoRR abs/2402.09666 (2024) - [i215]Haoxiang Wang, Yong Lin, Wei Xiong, Rui Yang, Shizhe Diao, Shuang Qiu, Han Zhao, Tong Zhang:
Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards. CoRR abs/2402.18571 (2024) - [i214]Xunpeng Huang, Hanze Dong, Difan Zou, Tong Zhang:
An Improved Analysis of Langevin Algorithms with Prior Diffusion for Non-Log-Concave Sampling. CoRR abs/2403.06183 (2024) - [i213]Renjie Pi, Tianyang Han, Wei Xiong, Jipeng Zhang, Runtao Liu, Rui Pan, Tong Zhang:
Strengthening Multimodal Large Language Model with Bootstrapped Preference Optimization. CoRR abs/2403.08730 (2024) - [i212]Qizhou Wang, Yong Lin, Yongqiang Chen, Ludwig Schmidt, Bo Han, Tong Zhang:
Do CLIPs Always Generalize Better than ImageNet Models? CoRR abs/2403.11497 (2024) - [i211]Yifan Hao, Yong Lin, Difan Zou, Tong Zhang:
On the Benefits of Over-parameterization for Out-of-Distribution Generalization. CoRR abs/2403.17592 (2024) - [i210]Rui Pan, Xiang Liu, Shizhe Diao, Renjie Pi, Jipeng Zhang, Chi Han, Tong Zhang:
LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning. CoRR abs/2403.17919 (2024) - [i209]Zixuan Zhang, Revanth Gangi Reddy, Kevin Small, Tong Zhang, Heng Ji:
Towards Better Generalization in Open-Domain Question Answering by Mitigating Context Memorization. CoRR abs/2404.01652 (2024) - [i208]Miao Lu, Han Zhong, Tong Zhang, Jose H. Blanchet:
Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm. CoRR abs/2404.03578 (2024) - [i207]Hanze Dong, Wei Xiong, Bo Pang, Haoxiang Wang, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, Tong Zhang:
RLHF Workflow: From Reward Modeling to Online RLHF. CoRR abs/2405.07863 (2024) - [i206]Cheng Niu, Xingguang Wang, Xuxin Cheng, Juntong Song, Tong Zhang:
Enhancing Dialogue State Tracking Models through LLM-backed User-Agents Simulation. CoRR abs/2405.13037 (2024) - [i205]Xunpeng Huang, Difan Zou, Hanze Dong, Yi Zhang, Yi-An Ma, Tong Zhang:
Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference. CoRR abs/2405.16387 (2024) - [i204]Xunpeng Huang, Difan Zou, Yi-An Ma, Hanze Dong, Tong Zhang:
Faster Sampling via Stochastic Gradient Proximal Sampler. CoRR abs/2405.16734 (2024) - [i203]Rui Yang, Ruomeng Ding, Yong Lin, Huan Zhang, Tong Zhang:
Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs. CoRR abs/2406.10216 (2024) - [i202]Cheng Niu, Yang Guan, Yuanhao Wu, Juno Zhu, Juntong Song, Randy Zhong, Kaihua Zhu, Siliang Xu, Shizhe Diao, Tong Zhang:
VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning. CoRR abs/2406.10289 (2024) - [i201]Haoxiang Wang, Wei Xiong, Tengyang Xie, Han Zhao, Tong Zhang:
Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts. CoRR abs/2406.12845 (2024) - [i200]Yuxing Liu, Rui Pan, Tong Zhang:
Large Batch Analysis for Adagrad Under Anisotropic Smoothness. CoRR abs/2406.15244 (2024) - [i199]Rui Pan, Jipeng Zhang, Xingyuan Pan, Renjie Pi, Xiaoyu Wang, Tong Zhang:
ScaleBiO: Scalable Bilevel Optimization for LLM Data Reweighting. CoRR abs/2406.19976 (2024) - [i198]Ruida Wang, Jipeng Zhang, Yizhen Jia, Rui Pan, Shizhe Diao, Renjie Pi, Tong Zhang:
TheoremLlama: Transforming General-Purpose LLMs into Lean4 Experts. CoRR abs/2407.03203 (2024) - [i197]Dake Zhang, Boxiang Lyu, Shuang Qiu, Mladen Kolar, Tong Zhang:
Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning. CoRR abs/2407.07631 (2024) - [i196]Rui Xie, Asad Ul Haq, Linsen Ma, Krystal Sun, Sanchari Sen, Swagath Venkataramani, Liu Liu, Tong Zhang:
SmartQuant: CXL-based AI Model Store in Support of Runtime Configurable Weight Quantization. CoRR abs/2407.15866 (2024) - [i195]Shuang Qiu, Dake Zhang, Rui Yang, Boxiang Lyu, Tong Zhang:
Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning. CoRR abs/2407.17466 (2024) - [i194]Yifei He, Yuzheng Hu, Yong Lin, Tong Zhang, Han Zhao:
Localize-and-Stitch: Efficient Model Merging via Sparse Task Arithmetic. CoRR abs/2408.13656 (2024) - [i193]Renjie Pi, Jianshu Zhang, Tianyang Han, Jipeng Zhang, Rui Pan, Tong Zhang:
Personalized Visual Instruction Tuning. CoRR abs/2410.07113 (2024) - [i192]Jipeng Zhang, Jianshu Zhang, Yuanzhe Li, Renjie Pi, Rui Pan, Runtao Liu, Ziqiang Zheng, Tong Zhang:
Bridge-Coder: Unlocking LLMs' Potential to Overcome Language Gaps in Low-Resource Code. CoRR abs/2410.18957 (2024) - [i191]Xiaoyu Wang, Xuxing Chen, Shiqian Ma, Tong Zhang:
Fully First-Order Methods for Decentralized Bilevel Optimization. CoRR abs/2410.19319 (2024) - 2023
- [j81]Haishan Ye, Luo Luo, Ziang Zhou, Tong Zhang:
Multi-Consensus Decentralized Accelerated Gradient Descent. J. Mach. Learn. Res. 24: 306:1-306:50 (2023) - [j80]Shizhe Diao, Zhichao Huang, Ruijia Xu, Xuechun Li, Yong Lin, Xiao Zhou, Tong Zhang:
Black-Box Prompt Learning for Pre-trained Language Models. Trans. Mach. Learn. Res. 2023 (2023) - [j79]Hanze Dong, Wei Xiong, Deepanshu Goyal, Yihan Zhang, Winnie Chow, Rui Pan, Shizhe Diao, Jipeng Zhang, Kashun Shum, Tong Zhang:
RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment. Trans. Mach. Learn. Res. 2023 (2023) - [c222]Yue He, Xinwei Shen, Renzhe Xu, Tong Zhang, Yong Jiang, Wenchao Zou, Peng Cui:
Covariate-Shift Generalization via Random Sample Weighting. AAAI 2023: 11828-11836 - [c221]Shizhe Diao, Tianyang Xu, Ruijia Xu, Jiawei Wang, Tong Zhang:
Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models' Memories. ACL (1) 2023: 5113-5129 - [c220]Xun Qian, Hanze Dong, Tong Zhang, Peter Richtárik:
Catalyst Acceleration of Error Compensated Methods Leads to Better Communication Complexity. AISTATS 2023: 615-649 - [c219]Alekh Agarwal, Yujia Jin, Tong Zhang:
VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation. COLT 2023: 987-1063 - [c218]Heyang Zhao, Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu:
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency. COLT 2023: 4977-5020 - [c217]Kashun Shum, Shizhe Diao, Tong Zhang:
Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data. EMNLP (Findings) 2023: 12113-12139 - [c216]Shizhe Diao, Yongyu Lei, Liangming Pan, Tianqing Fang, Wangchunshu Zhou, Sedrick Scott Keh, Min-Yen Kan, Tong Zhang:
Doolittle: Benchmarks and Corpora for Academic Writing Formalization. EMNLP 2023: 13093-13111 - [c215]Renjie Pi, Jiahui Gao, Shizhe Diao, Rui Pan, Hanze Dong, Jipeng Zhang, Lewei Yao, Jianhua Han, Hang Xu, Lingpeng Kong, Tong Zhang:
DetGPT: Detect What You Need via Reasoning. EMNLP 2023: 14172-14189 - [c214]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Liwei Wang, Tong Zhang:
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game. ICLR 2023 - [c213]Hanze Dong, Xi Wang, Yong Lin, Tong Zhang:
Particle-based Variational Inference with Preconditioned Functional Gradient Flow. ICLR 2023 - [c212]Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, Tong Zhang:
On the Convergence of Federated Averaging with Cyclic Client Participation. ICML 2023: 5677-5721 - [c211]Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi:
Beyond Uniform Lipschitz Condition in Differentially Private Optimization. ICML 2023: 7066-7101 - [c210]Jonathan Lee, Alekh Agarwal, Christoph Dann, Tong Zhang:
Learning in POMDPs is Sample-Efficient with Hindsight Observability. ICML 2023: 18733-18773 - [c209]Xiaoyu Wang, Mikael Johansson, Tong Zhang:
Generalized Polyak Step Size for First Order Optimization with Momentum. ICML 2023: 35836-35863 - [c208]Rui Yang, Lin Yong, Xiaoteng Ma, Hao Hu, Chongjie Zhang, Tong Zhang:
What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL? ICML 2023: 39543-39571 - [c207]Chenlu Ye, Wei Xiong, Quanquan Gu, Tong Zhang:
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes. ICML 2023: 39834-39863 - [c206]Linsen Ma, Rui Xie, Tong Zhang:
ZipKV: In-Memory Key-Value Store with Built-In Data Compression. ISMM 2023: 150-162 - [c205]Han Zhong, Tong Zhang:
A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes. NeurIPS 2023 - [c204]Jose H. Blanchet, Miao Lu, Tong Zhang, Han Zhong:
Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage. NeurIPS 2023 - [c203]Rie Johnson, Tong Zhang:
Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training. NeurIPS 2023 - [c202]Yuanshi Liu, Cong Fang, Tong Zhang:
Double Randomized Underdamped Langevin with Dimension-Independent Convergence Guarantee. NeurIPS 2023 - [c201]Shuang Qiu, Ziyu Dai, Han Zhong, Zhaoran Wang, Zhuoran Yang, Tong Zhang:
Posterior Sampling for Competitive RL: Function Approximation and Partial Observation. NeurIPS 2023 - [c200]Chenlu Ye, Rui Yang, Quanquan Gu, Tong Zhang:
Corruption-Robust Offline Reinforcement Learning with General Function Approximation. NeurIPS 2023 - [c199]Shizhe Diao, Sedrick Scott Keh, Liangming Pan, Zhiliang Tian, Yan Song, Tong Zhang:
Hashtag-Guided Low-Resource Tweet Classification. WWW 2023: 1415-1426 - [i190]Xiao Zhou, Yong Lin, Renjie Pi, Weizhong Zhang, Renzhe Xu, Peng Cui, Tong Zhang:
Model Agnostic Sample Reweighting for Out-of-Distribution Learning. CoRR abs/2301.09819 (2023) - [i189]Xiao Zhou, Renjie Pi, Weizhong Zhang, Yong Lin, Tong Zhang:
Probabilistic Bilevel Coreset Selection. CoRR abs/2301.09880 (2023) - [i188]Jonathan N. Lee, Alekh Agarwal, Christoph Dann, Tong Zhang:
Learning in POMDPs is Sample-Efficient with Hindsight Observability. CoRR abs/2301.13857 (2023) - [i187]Yae Jee Cho, Pranay Sharma, Gauri Joshi, Zheng Xu, Satyen Kale, Tong Zhang:
On the Convergence of Federated Averaging with Cyclic Client Participation. CoRR abs/2302.03109 (2023) - [i186]Shizhe Diao, Sedrick Scott Keh, Liangming Pan, Zhiliang Tian, Yan Song, Tong Zhang:
Hashtag-Guided Low-Resource Tweet Classification. CoRR abs/2302.10143 (2023) - [i185]Heyang Zhao, Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu:
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency. CoRR abs/2302.10371 (2023) - [i184]Shizhe Diao, Pengcheng Wang, Yong Lin, Tong Zhang:
Active Prompting with Chain-of-Thought for Large Language Models. CoRR abs/2302.12246 (2023) - [i183]Kashun Shum, Shizhe Diao, Tong Zhang:
Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data. CoRR abs/2302.12822 (2023) - [i182]Shihong Ding, Hanze Dong, Cong Fang, Zhouchen Lin, Tong Zhang:
Provable Particle-based Primal-Dual Algorithm for Mixed Nash Equilibrium. CoRR abs/2303.00970 (2023) - [i181]Jianqing Fan, Cong Fang, Yihong Gu, Tong Zhang:
Environment Invariant Linear Least Squares. CoRR abs/2303.03092 (2023) - [i180]Hanze Dong, Wei Xiong, Deepanshu Goyal, Rui Pan, Shizhe Diao, Jipeng Zhang, Kashun Shum, Tong Zhang:
RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment. CoRR abs/2304.06767 (2023) - [i179]Han Zhong, Tong Zhang:
A Theoretical Analysis of Optimistic Proximal Policy Optimization in Linear Markov Decision Processes. CoRR abs/2305.08841 (2023) - [i178]Jose H. Blanchet, Miao Lu, Tong Zhang, Han Zhong:
Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage. CoRR abs/2305.09659 (2023) - [i177]Renjie Pi, Jiahui Gao, Shizhe Diao, Rui Pan, Hanze Dong, Jipeng Zhang, Lewei Yao, Jianhua Han, Hang Xu, Lingpeng Kong, Tong Zhang:
DetGPT: Detect What You Need via Reasoning. CoRR abs/2305.14167 (2023) - [i176]Rui Yang, Yong Lin, Xiaoteng Ma, Hao Hu, Chongjie Zhang, Tong Zhang:
What is Essential for Unseen Goal Generalization of Offline Goal-conditioned RL? CoRR abs/2305.18882 (2023) - [i175]Rie Johnson, Tong Zhang:
Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training. CoRR abs/2306.00169 (2023) - [i174]Shizhe Diao, Tianyang Xu, Ruijia Xu, Jiawei Wang, Tong Zhang:
Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models Memories. CoRR abs/2306.05406 (2023) - [i173]Shizhe Diao, Rui Pan, Hanze Dong, Kashun Shum, Jipeng Zhang, Wei Xiong, Tong Zhang:
LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. CoRR abs/2306.12420 (2023) - [i172]Xunpeng Huang, Hanze Dong, Yifan Hao, Yian Ma, Tong Zhang:
Monte Carlo Sampling without Isoperimetry: A Reverse Diffusion Approach. CoRR abs/2307.02037 (2023) - [i171]Yong Lin, Chen Liu, Chenlu Ye, Qing Lian, Yuan Yao, Tong Zhang:
Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning. CoRR abs/2309.02476 (2023) - [i170]Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Yuan Yao, Tong Zhang:
Mitigating the Alignment Tax of RLHF. CoRR abs/2309.06256 (2023) - [i169]Helbert Paat, Qing Lian, Weilong Yao, Tong Zhang:
MEDL-U: Uncertainty-aware 3D Automatic Annotator based on Evidential Deep Learning. CoRR abs/2309.09599 (2023) - [i168]Yong Lin, Lu Tan, Yifan Hao, Honam Wong, Hanze Dong, Weizhong Zhang, Yujiu Yang, Tong Zhang:
Spurious Feature Diversification Improves Out-of-distribution Generalization. CoRR abs/2309.17230 (2023) - [i167]Rui Yang, Han Zhong, Jiawei Xu, Amy Zhang, Chongjie Zhang, Lei Han, Tong Zhang:
Towards Robust Offline Reinforcement Learning under Diverse Data Corruption. CoRR abs/2310.12955 (2023) - [i166]Chenlu Ye, Rui Yang, Quanquan Gu, Tong Zhang:
Corruption-Robust Offline Reinforcement Learning with General Function Approximation. CoRR abs/2310.14550 (2023) - [i165]Shuang Qiu, Ziyu Dai, Han Zhong, Zhaoran Wang, Zhuoran Yang, Tong Zhang:
Posterior Sampling for Competitive RL: Function Approximation and Partial Observation. CoRR abs/2310.19861 (2023) - [i164]Renjie Pi, Lewei Yao, Jiahui Gao, Jipeng Zhang, Tong Zhang:
PerceptionGPT: Effectively Fusing Visual Perception into LLM. CoRR abs/2311.06612 (2023) - [i163]Rui Pan, Shuo Xing, Shizhe Diao, Xiang Liu, Kashun Shum, Jipeng Zhang, Tong Zhang:
Plum: Prompt Learning using Metaheuristic. CoRR abs/2311.08364 (2023) - [i162]Hanning Zhang, Shizhe Diao, Yong Lin, Yi R. Fung, Qing Lian, Xingyao Wang, Yangyi Chen, Heng Ji, Tong Zhang:
R-Tuning: Teaching Large Language Models to Refuse Unknown Questions. CoRR abs/2311.09677 (2023) - [i161]Jiaqi Tang, Hao Lu, Xiaogang Xu, Ruizheng Wu, Sixing Hu, Tong Zhang, Tsz Wa Cheng, Ming Ge, Ying-Cong Chen, Fugee Tsung:
An Incremental Unified Framework for Small Defect Inspection. CoRR abs/2312.08917 (2023) - [i160]Wei Xiong, Hanze Dong, Chenlu Ye, Han Zhong, Nan Jiang, Tong Zhang:
Gibbs Sampling from Human Feedback: A Provable KL- constrained Framework for RLHF. CoRR abs/2312.11456 (2023) - [i159]Rui Pan, Yuxing Liu, Xiaoyu Wang, Tong Zhang:
Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise. CoRR abs/2312.14567 (2023) - 2022
- [j78]Yoav Freund, Yi-An Ma, Tong Zhang:
When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint. J. Mach. Learn. Res. 23: 214:1-214:32 (2022) - [j77]Xinwei Shen, Furui Liu, Hanze Dong, Qing Lian, Zhitang Chen, Tong Zhang:
Weakly Supervised Disentangled Generative Causal Representation Learning. J. Mach. Learn. Res. 23: 241:1-241:55 (2022) - [j76]Minghan Yang, Andre Milzarek, Zaiwen Wen, Tong Zhang:
A stochastic extra-step quasi-Newton method for nonsmooth nonconvex optimization. Math. Program. 194(1): 257-303 (2022) - [j75]Tong Zhang:
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning. SIAM J. Math. Data Sci. 4(2): 834-857 (2022) - [j74]Cong Fang, Yihong Gu, Weizhong Zhang, Tong Zhang:
Convex Formulation of Overparameterized Deep Neural Networks. IEEE Trans. Inf. Theory 68(8): 5340-5352 (2022) - [c198]Tong Zhang, Wei Ye, Baosong Yang, Long Zhang, Xingzhang Ren, Dayiheng Liu, Jinan Sun, Shikun Zhang, Haibo Zhang, Wen Zhao:
Frequency-Aware Contrastive Learning for Neural Machine Translation. AAAI 2022: 11712-11720 - [c197]Ying Su, Hongming Zhang, Yangqiu Song, Tong Zhang:
Rare and Zero-shot Word Sense Disambiguation using Z-Reweighting. ACL (1) 2022: 4713-4723 - [c196]Zile Qiao, Wei Ye, Tong Zhang, Tong Mo, Weiping Li, Shikun Zhang:
Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering over Knowledge Graphs. COLING 2022: 1813-1822 - [c195]Ying Su, Hongming Zhang, Yangqiu Song, Tong Zhang:
Multilingual Word Sense Disambiguation with Unified Sense Representation. COLING 2022: 4193-4202 - [c194]Alekh Agarwal, Tong Zhang:
Minimax Regret Optimization for Robust Machine Learning under Distribution Shift. COLT 2022: 2704-2729 - [c193]Alekh Agarwal, Tong Zhang:
Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling. COLT 2022: 2776-2814 - [c192]Qing Lian, Botao Ye, Ruijia Xu, Weilong Yao, Tong Zhang:
Exploring Geometric Consistency for Monocular 3D Object Detection. CVPR 2022: 1675-1684 - [c191]Yong Lin, Hanze Dong, Hao Wang, Tong Zhang:
Bayesian Invariant Risk Minimization. CVPR 2022: 16000-16009 - [c190]Qing Lian, Yanbo Xu, Weilong Yao, Yingcong Chen, Tong Zhang:
Semi-supervised Monocular 3D Object Detection by Multi-view Consistency. ECCV (8) 2022: 715-731 - [c189]Ying Su, Zihao Wang, Tianqing Fang, Hongming Zhang, Yangqiu Song, Tong Zhang:
MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation. EMNLP (Findings) 2022: 1339-1351 - [c188]Ziniu Li, Yingru Li, Yushun Zhang, Tong Zhang, Zhi-Quan Luo:
HyperDQN: A Randomized Exploration Method for Deep Reinforcement Learning. ICLR 2022 - [c187]Rui Pan, Haishan Ye, Tong Zhang:
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums. ICLR 2022 - [c186]Claudio Gentile, Zhilei Wang, Tong Zhang:
Achieving Minimax Rates in Pool-Based Batch Active Learning. ICML 2022: 7339-7367 - [c185]Hao Liu, Minshuo Chen, Siawpeng Er, Wenjing Liao, Tong Zhang, Tuo Zhao:
Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint. ICML 2022: 13669-13703 - [c184]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Tong Zhang:
A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games. ICML 2022: 24496-24523 - [c183]Renzhe Xu, Xingxuan Zhang, Zheyan Shen, Tong Zhang, Peng Cui:
A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization. ICML 2022: 24803-24829 - [c182]Han Zhong, Wei Xiong, Jiyuan Tan, Liwei Wang, Tong Zhang, Zhaoran Wang, Zhuoran Yang:
Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets. ICML 2022: 27117-27142 - [c181]Xiao Zhou, Yong Lin, Renjie Pi, Weizhong Zhang, Renzhe Xu, Peng Cui, Tong Zhang:
Model Agnostic Sample Reweighting for Out-of-Distribution Learning. ICML 2022: 27203-27221 - [c180]Xiao Zhou, Yong Lin, Weizhong Zhang, Tong Zhang:
Sparse Invariant Risk Minimization. ICML 2022: 27222-27244 - [c179]Xiao Zhou, Renjie Pi, Weizhong Zhang, Yong Lin, Zonghao Chen, Tong Zhang:
Probabilistic Bilevel Coreset Selection. ICML 2022: 27287-27302 - [c178]Alekh Agarwal, Tong Zhang:
Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity. NeurIPS 2022 - [c177]Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu:
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions. NeurIPS 2022 - [i158]Shizhe Diao, Xuechun Li, Yong Lin, Zhichao Huang, Tong Zhang:
Black-box Prompt Learning for Pre-trained Language Models. CoRR abs/2201.08531 (2022) - [i157]Alekh Agarwal, Tong Zhang:
Minimax Regret Optimization for Robust Machine Learning under Distribution Shift. CoRR abs/2202.05436 (2022) - [i156]Claudio Gentile, Zhilei Wang, Tong Zhang:
Achieving Minimax Rates in Pool-Based Batch Active Learning. CoRR abs/2202.05448 (2022) - [i155]Han Zhong, Wei Xiong, Jiyuan Tan, Liwei Wang, Tong Zhang, Zhaoran Wang, Zhuoran Yang:
Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets. CoRR abs/2202.07511 (2022) - [i154]Alekh Agarwal, Tong Zhang:
Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling. CoRR abs/2203.08248 (2022) - [i153]Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu:
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions. CoRR abs/2205.06811 (2022) - [i152]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Liwei Wang, Tong Zhang:
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game. CoRR abs/2205.15512 (2022) - [i151]Yi-An Ma, Teodor Vanislavov Marinov, Tong Zhang:
Dimension Independent Generalization of DP-SGD for Overparameterized Smooth Convex Optimization. CoRR abs/2206.01836 (2022) - [i150]Hao Liu, Minshuo Chen, Siawpeng Er, Wenjing Liao, Tong Zhang, Tuo Zhao:
Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint. CoRR abs/2206.04569 (2022) - [i149]Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, Tong Zhang:
On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data. CoRR abs/2206.04723 (2022) - [i148]Alekh Agarwal, Tong Zhang:
Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity. CoRR abs/2206.07659 (2022) - [i147]Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi:
Beyond Uniform Lipschitz Condition in Differentially Private Optimization. CoRR abs/2206.10713 (2022) - [i146]Christoph Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert:
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning. CoRR abs/2208.10904 (2022) - [i145]Zile Qiao, Wei Ye, Tong Zhang, Tong Mo, Weiping Li, Shikun Zhang:
Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering Over Knowledge Graphs. CoRR abs/2209.00870 (2022) - [i144]Xinwei Shen, Kani Chen, Tong Zhang:
Asymptotic Statistical Analysis of f-divergence GAN. CoRR abs/2209.06853 (2022) - [i143]Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen, Tong Zhang:
A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games. CoRR abs/2210.01907 (2022) - [i142]Ying Su, Hongming Zhang, Yangqiu Song, Tong Zhang:
Multilingual Word Sense Disambiguation with Unified Sense Representation. CoRR abs/2210.07447 (2022) - [i141]Ying Su, Zihao Wang, Tianqing Fang, Hongming Zhang, Yangqiu Song, Tong Zhang:
MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation. CoRR abs/2210.07570 (2022) - [i140]Han Zhong, Wei Xiong, Sirui Zheng, Liwei Wang, Zhaoran Wang, Zhuoran Yang, Tong Zhang:
GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond. CoRR abs/2211.01962 (2022) - [i139]Hanze Dong, Shizhe Diao, Weizhong Zhang, Tong Zhang:
Normalizing Flow with Variational Latent Representation. CoRR abs/2211.11638 (2022) - [i138]Hanze Dong, Xi Wang, Yong Lin, Tong Zhang:
Particle-based Variational Inference with Preconditioned Functional Gradient Flow. CoRR abs/2211.13954 (2022) - [i137]Rui Pan, Shizhe Diao, Jianlin Chen, Tong Zhang:
ExtremeBERT: A Toolkit for Accelerating Pretraining of Customized BERT. CoRR abs/2211.17201 (2022) - [i136]Chenlu Ye, Wei Xiong, Quanquan Gu, Tong Zhang:
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes. CoRR abs/2212.05949 (2022) - [i135]Alekh Agarwal, Yujia Jin, Tong Zhang:
VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation. CoRR abs/2212.06069 (2022) - 2021
- [j73]Haishan Ye, Tong Zhang:
DeEPCA: Decentralized Exact PCA with Linear Convergence Rate. J. Mach. Learn. Res. 22: 238:1-238:27 (2021) - [j72]Rie Johnson, Tong Zhang:
A Framework of Composite Functional Gradient Methods for Generative Adversarial Models. IEEE Trans. Pattern Anal. Mach. Intell. 43(1): 17-32 (2021) - [j71]Cong Fang, Hanze Dong, Tong Zhang:
Mathematical Models of Overparameterized Neural Networks. Proc. IEEE 109(5): 683-703 (2021) - [j70]Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Basar:
Finite-Sample Analysis for Decentralized Batch Multiagent Reinforcement Learning With Networked Agents. IEEE Trans. Autom. Control. 66(12): 5925-5940 (2021) - [j69]Jun Song, Yueyang Wang, Siliang Tang, Yin Zhang, Zhigang Chen, Zhongfei Zhang, Tong Zhang, Fei Wu:
Local-Global Memory Neural Network for Medication Prediction. IEEE Trans. Neural Networks Learn. Syst. 32(4): 1723-1736 (2021) - [c176]Shizhe Diao, Ruijia Xu, Hongjin Su, Yilei Jiang, Yan Song, Tong Zhang:
Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation. ACL/IJCNLP (1) 2021: 3336-3349 - [c175]Tong Zhang, Long Zhang, Wei Ye, Bo Li, Jinan Sun, Xiaoyu Zhu, Wen Zhao, Shikun Zhang:
Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation. ACL/IJCNLP (1) 2021: 3970-3979 - [c174]Shizhe Diao, Xinwei Shen, Kashun Shum, Yan Song, Tong Zhang:
TILGAN: Transformer-based Implicit Latent GAN for Diverse and Coherent Text Generation. ACL/IJCNLP (Findings) 2021: 4844-4858 - [c173]Cong Fang, Jason D. Lee, Pengkun Yang, Tong Zhang:
Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks. COLT 2021: 1887-1936 - [c172]Zhichao Huang, Xintong Han, Jia Xu, Tong Zhang:
Few-Shot Human Motion Transfer by Personalized Geometry and Texture Modeling. CVPR 2021: 2297-2306 - [c171]Xiao Zhou, Weizhong Zhang, Hang Xu, Tong Zhang:
Effective Sparsification of Neural Networks With Global Sparsity Constraint. CVPR 2021: 3599-3608 - [c170]Yawen Duan, Xin Chen, Hang Xu, Zewei Chen, Xiaodan Liang, Tong Zhang, Zhenguo Li:
TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search. CVPR 2021: 5251-5260 - [c169]Lewei Yao, Renjie Pi, Hang Xu, Wei Zhang, Zhenguo Li, Tong Zhang:
Joint-DetNAS: Upgrade Your Detector With NAS, Pruning and Dynamic Distillation. CVPR 2021: 10175-10184 - [c168]Duo Li, Jie Hu, Changhu Wang, Xiangtai Li, Qi She, Lei Zhu, Tong Zhang, Qifeng Chen:
Involution: Inverting the Inherence of Convolution for Visual Recognition. CVPR 2021: 12321-12330 - [c167]Xiangyu Xi, Wei Ye, Tong Zhang, Quanxiu Wang, Shikun Zhang, Huixing Jiang, Wei Wu:
Improving Event Detection by Exploiting Label Hierarchy. ICASSP 2021: 7688-7692 - [c166]Lewei Yao, Renjie Pi, Hang Xu, Wei Zhang, Zhenguo Li, Tong Zhang:
G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation. ICCV 2021: 3571-3580 - [c165]Yuhui Ding, Quanming Yao, Huan Zhao, Tong Zhang:
DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks. KDD 2021: 279-288 - [c164]Long Zhang, Tong Zhang, Haibo Zhang, Baosong Yang, Wei Ye, Shikun Zhang:
Multi-Hop Transformer for Document-Level Machine Translation. NAACL-HLT 2021: 3953-3963 - [c163]Christoph Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert:
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning. NeurIPS 2021: 12040-12051 - [c162]Xiao Zhou, Weizhong Zhang, Zonghao Chen, Shizhe Diao, Tong Zhang:
Efficient Neural Network Training via Forward and Backward Propagation Sparsification. NeurIPS 2021: 15216-15229 - [c161]Xun Qian, Peter Richtárik, Tong Zhang:
Error Compensated Distributed SGD Can Be Accelerated. NeurIPS 2021: 30401-30413 - [e2]Marina Meila, Tong Zhang:
Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event. Proceedings of Machine Learning Research 139, PMLR 2021 [contents] - [i134]Haishan Ye, Tong Zhang:
DeEPCA: Decentralized Exact PCA with Linear Convergence Rate. CoRR abs/2102.03990 (2021) - [i133]Duo Li, Jie Hu, Changhu Wang, Xiangtai Li, Qi She, Lei Zhu, Tong Zhang, Qifeng Chen:
Involution: Inverting the Inherence of Convolution for Visual Recognition. CoRR abs/2103.06255 (2021) - [i132]Zhichao Huang, Xintong Han, Jia Xu, Tong Zhang:
Few-Shot Human Motion Transfer by Personalized Geometry and Texture Modeling. CoRR abs/2103.14338 (2021) - [i131]Qing Lian, Botao Ye, Ruijia Xu, Weilong Yao, Tong Zhang:
Geometry-aware data augmentation for monocular 3D object detection. CoRR abs/2104.05858 (2021) - [i130]Yan Song, Tong Zhang, Yonggang Wang, Kai-Fu Lee:
ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders. CoRR abs/2105.01279 (2021) - [i129]Xiao Zhou, Weizhong Zhang, Hang Xu, Tong Zhang:
Effective Sparsification of Neural Networks with Global Sparsity Constraint. CoRR abs/2105.01571 (2021) - [i128]Yawen Duan, Xin Chen, Hang Xu, Zewei Chen, Xiaodan Liang, Tong Zhang, Zhenguo Li:
TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search. CoRR abs/2105.11871 (2021) - [i127]Lewei Yao, Renjie Pi, Hang Xu, Wei Zhang, Zhenguo Li, Tong Zhang:
Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation. CoRR abs/2105.12971 (2021) - [i126]Hanting Chen, Yunhe Wang, Chang Xu, Chao Xu, Chunjing Xu, Tong Zhang:
Adder Neural Networks. CoRR abs/2105.14202 (2021) - [i125]Luo Luo, Guangzeng Xie, Tong Zhang, Zhihua Zhang:
Near Optimal Stochastic Algorithms for Finite-Sum Unbalanced Convex-Concave Minimax Optimization. CoRR abs/2106.01761 (2021) - [i124]Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horváth, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu:
A Field Guide to Federated Optimization. CoRR abs/2107.06917 (2021) - [i123]Lewei Yao, Renjie Pi, Hang Xu, Wei Zhang, Zhenguo Li, Tong Zhang:
G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation. CoRR abs/2108.07482 (2021) - [i122]Tong Zhang:
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning. CoRR abs/2110.00871 (2021) - [i121]Yoav Freund, Yi-An Ma, Tong Zhang:
When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint. CoRR abs/2110.01827 (2021) - [i120]Rui Pan, Haishan Ye, Tong Zhang:
Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums. CoRR abs/2110.14109 (2021) - [i119]Renzhe Xu, Peng Cui, Zheyan Shen, Xingxuan Zhang, Tong Zhang:
Why Stable Learning Works? A Theory of Covariate Shift Generalization. CoRR abs/2111.02355 (2021) - [i118]Xiao Zhou, Weizhong Zhang, Zonghao Chen, Shizhe Diao, Tong Zhang:
Efficient Neural Network Training via Forward and Backward Propagation Sparsification. CoRR abs/2111.05685 (2021) - [i117]Tong Zhang, Wei Ye, Baosong Yang, Long Zhang, Xingzhang Ren, Dayiheng Liu, Jinan Sun, Shikun Zhang, Haibo Zhang, Wen Zhao:
Frequency-Aware Contrastive Learning for Neural Machine Translation. CoRR abs/2112.14484 (2021) - 2020
- [j68]Baoyuan Wu, Li Shen, Tong Zhang, Bernard Ghanem:
MAP Inference Via ℓ 2-Sphere Linear Program Reformulation. Int. J. Comput. Vis. 128(7): 1913-1936 (2020) - [j67]Conghui Tan, Yuqiu Qian, Shiqian Ma, Tong Zhang:
Accelerated dual-averaging primal-dual method for composite convex minimization. Optim. Methods Softw. 35(4): 741-766 (2020) - [j66]Wenhan Luo, Peng Sun, Fangwei Zhong, Wei Liu, Tong Zhang, Yizhou Wang:
End-to-End Active Object Tracking and Its Real-World Deployment via Reinforcement Learning. IEEE Trans. Pattern Anal. Mach. Intell. 42(6): 1317-1332 (2020) - [j65]Shixiang Chen, Shiqian Ma, Anthony Man-Cho So, Tong Zhang:
Proximal Gradient Method for Nonsmooth Optimization over the Stiefel Manifold. SIAM J. Optim. 30(1): 210-239 (2020) - [c160]Zheyan Shen, Peng Cui, Tong Zhang, Kun Kuang:
Stable Learning via Sample Reweighting. AAAI 2020: 5692-5699 - [c159]Yuanhe Tian, Yan Song, Fei Xia, Tong Zhang, Yonggang Wang:
Improving Chinese Word Segmentation with Wordhood Memory Networks. ACL 2020: 8274-8285 - [c158]Yuanhe Tian, Yan Song, Xiang Ao, Fei Xia, Xiaojun Quan, Tong Zhang, Yonggang Wang:
Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge. ACL 2020: 8286-8296 - [c157]Chaoyang He, Haishan Ye, Li Shen, Tong Zhang:
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation. CVPR 2020: 11990-11999 - [c156]Qi Chang, Hui Qu, Yikai Zhang, Mert R. Sabuncu, Chao Chen, Tong Zhang, Dimitris N. Metaxas:
Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data. CVPR 2020: 13853-13863 - [c155]Tong Zhang, Wei Ye, Xiangyu Xi, Long Zhang, Shikun Zhang, Wen Zhao:
Leveraging Human Prior Knowledge to Learn Sense Representations. ECAI 2020: 2306-2313 - [c154]Xin Chen, Yawen Duan, Zewei Chen, Hang Xu, Zihao Chen, Xiaodan Liang, Tong Zhang, Zhenguo Li:
CATCH: Context-Based Meta Reinforcement Learning for Transferrable Architecture Search. ECCV (19) 2020: 185-202 - [c153]Yuanhe Tian, Yan Song, Fei Xia, Tong Zhang:
Improving Constituency Parsing with Span Attention. EMNLP (Findings) 2020: 1691-1703 - [c152]Shizhe Diao, Jiaxin Bai, Yan Song, Tong Zhang, Yonggang Wang:
ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations. EMNLP (Findings) 2020: 4729-4740 - [c151]Zhichao Huang, Tong Zhang:
Black-Box Adversarial Attack with Transferable Model-based Embedding. ICLR 2020 - [c150]Rie Johnson, Tong Zhang:
Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization. ICML 2020: 4921-4930 - [c149]Zheyan Shen, Peng Cui, Jiashuo Liu, Tong Zhang, Bo Li, Zhitang Chen:
Stable Learning via Differentiated Variable Decorrelation. KDD 2020: 2185-2193 - [c148]Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang:
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks. NeurIPS 2020 - [c147]Yihong Gu, Weizhong Zhang, Cong Fang, Jason D. Lee, Tong Zhang:
How to Characterize The Landscape of Overparameterized Convolutional Neural Networks. NeurIPS 2020 - [c146]Kai Han, Yunhe Wang, Qiulin Zhang, Wei Zhang, Chunjing Xu, Tong Zhang:
Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets. NeurIPS 2020 - [c145]Guilin Li, Junlei Zhang, Yunhe Wang, Chuanjian Liu, Matthias Tan, Yunfeng Lin, Wei Zhang, Jiashi Feng, Tong Zhang:
Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts. NeurIPS 2020 - [c144]Luo Luo, Haishan Ye, Zhichao Huang, Tong Zhang:
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. NeurIPS 2020 - [c143]Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T. Kwok, Tong Zhang:
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS. NeurIPS 2020 - [c142]Haishan Ye, Ziang Zhou, Luo Luo, Tong Zhang:
Decentralized Accelerated Proximal Gradient Descent. NeurIPS 2020 - [i116]Luo Luo, Haishan Ye, Tong Zhang:
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems. CoRR abs/2001.03724 (2020) - [i115]Conghui Tan, Yuqiu Qian, Shiqian Ma, Tong Zhang:
Accelerated Dual-Averaging Primal-Dual Method for Composite Convex Minimization. CoRR abs/2001.05537 (2020) - [i114]Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang:
Mean-Field Analysis of Two-Layer Neural Networks: Non-Asymptotic Rates and Generalization Bounds. CoRR abs/2002.04026 (2020) - [i113]Xinwei Shen, Tong Zhang, Kani Chen:
Bidirectional Generative Modeling Using Adversarial Gradient Estimation. CoRR abs/2002.09161 (2020) - [i112]Chaoyang He, Haishan Ye, Li Shen, Tong Zhang:
MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation. CoRR abs/2003.12238 (2020) - [i111]Haishan Ye, Luo Luo, Ziang Zhou, Tong Zhang:
Multi-consensus Decentralized Accelerated Gradient Descent. CoRR abs/2005.00797 (2020) - [i110]Qi Chang, Hui Qu, Yikai Zhang, Mert R. Sabuncu, Chao Chen, Tong Zhang, Dimitris N. Metaxas:
Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data. CoRR abs/2006.00080 (2020) - [i109]Jason Ge, Xingguo Li, Haoming Jiang, Han Liu, Tong Zhang, Mengdi Wang, Tuo Zhao:
Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python. CoRR abs/2006.15261 (2020) - [i108]Rie Johnson, Tong Zhang:
Guided Learning of Nonconvex Models through Successive Functional Gradient Optimization. CoRR abs/2006.16840 (2020) - [i107]Cong Fang, Jason D. Lee, Pengkun Yang, Tong Zhang:
Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks. CoRR abs/2007.01452 (2020) - [i106]Xin Chen, Yawen Duan, Zewei Chen, Hang Xu, Zihao Chen, Xiaodan Liang, Tong Zhang, Zhenguo Li:
CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search. CoRR abs/2007.09380 (2020) - [i105]Zhichao Huang, Yaowei Huang, Tong Zhang:
CorrAttack: Black-box Adversarial Attack with Structured Search. CoRR abs/2010.01250 (2020) - [i104]Xinwei Shen, Furui Liu, Hanze Dong, Qing Lian, Zhitang Chen, Tong Zhang:
Disentangled Generative Causal Representation Learning. CoRR abs/2010.02637 (2020) - [i103]Yuhui Ding, Quanming Yao, Tong Zhang:
Propagation Model Search for Graph Neural Networks. CoRR abs/2010.03250 (2020) - [i102]Yuanhe Tian, Yan Song, Fei Xia, Tong Zhang:
Improving Constituency Parsing with Span Attention. CoRR abs/2010.07543 (2020) - [i101]Kai Han, Yunhe Wang, Qiulin Zhang, Wei Zhang, Chunjing Xu, Tong Zhang:
Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets. CoRR abs/2010.14819 (2020) - [i100]Bochao Wang, Hang Xu, Jiajin Zhang, Chen Chen, Yixing Xu, Xiaozhi Fang, Ning Kang, Lanqing Hong, Chenhan Jiang, Xinyue Cai, Jiawei Li, Fengwei Zhou, Yong Li, Zhicheng Liu, Xinghao Chen, Kai Han, Han Shu, Dehua Song, Yunhe Wang, Wei Zhang, Chunjing Xu, Zhenguo Li, Wenzhi Liu, Tong Zhang:
VEGA: Towards an End-to-End Configurable AutoML Pipeline. CoRR abs/2011.01507 (2020) - [i99]Qi Chang, Zhennan Yan, Lohendran Baskaran, Hui Qu, Yikai Zhang, Tong Zhang, Shaoting Zhang, Dimitris N. Metaxas:
Multi-modal AsynDGAN: Learn From Distributed Medical Image Data without Sharing Private Information. CoRR abs/2012.08604 (2020) - [i98]Cong Fang, Hanze Dong, Tong Zhang:
Mathematical Models of Overparameterized Neural Networks. CoRR abs/2012.13982 (2020) - [i97]Haishan Ye, Wei Xiong, Tong Zhang:
PMGT-VR: A decentralized proximal-gradient algorithmic framework with variance reduction. CoRR abs/2012.15010 (2020)
2010 – 2019
- 2019
- [j64]Baoyuan Wu, Weidong Chen, Yanbo Fan, Yong Zhang, Jinlong Hou, Jie Liu, Tong Zhang:
Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning. IEEE Access 7: 172683-172693 (2019) - [j63]Lei Han, Lei Li, Feng Wen, Lei Zhong, Tong Zhang, Xiu-Feng Wan:
Graph-guided multi-task sparse learning model: a method for identifying antigenic variants of influenza A(H3N2) virus. Bioinform. 35(1): 77-87 (2019) - [j62]Jialei Wang, Tong Zhang:
Utilizing Second Order Information in Minibatch Stochastic Variance Reduced Proximal Iterations. J. Mach. Learn. Res. 20: 42:1-42:56 (2019) - [j61]Jason Ge, Xingguo Li, Haoming Jiang, Han Liu, Tong Zhang, Mengdi Wang, Tuo Zhao:
Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python. J. Mach. Learn. Res. 20: 44:1-44:5 (2019) - [j60]Luo Luo, Cheng Chen, Zhihua Zhang, Wu-Jun Li, Tong Zhang:
Robust Frequent Directions with Application in Online Learning. J. Mach. Learn. Res. 20: 45:1-45:41 (2019) - [j59]Kean Ming Tan, Junwei Lu, Tong Zhang, Han Liu:
Layer-Wise Learning Strategy for Nonparametric Tensor Product Smoothing Spline Regression and Graphical Models. J. Mach. Learn. Res. 20: 119:1-119:38 (2019) - [c141]Zi-Yi Dou, Zhaopeng Tu, Xing Wang, Longyue Wang, Shuming Shi, Tong Zhang:
Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement. AAAI 2019: 86-93 - [c140]Xiang Kong, Zhaopeng Tu, Shuming Shi, Eduard H. Hovy, Tong Zhang:
Neural Machine Translation with Adequacy-Oriented Learning. AAAI 2019: 6618-6625 - [c139]Miaofeng Liu, Yan Song, Hongbin Zou, Tong Zhang:
Reinforced Training Data Selection for Domain Adaptation. ACL (1) 2019: 1957-1968 - [c138]Xin-Rong Gong, Jian-Xiu Jin, Tong Zhang:
Sentiment Analysis Using Autoregressive Language Modeling and Broad Learning System. BIBM 2019: 1130-1134 - [c137]Cong Fang, Zhouchen Lin, Tong Zhang:
Sharp Analysis for Nonconvex SGD Escaping from Saddle Points. COLT 2019: 1192-1234 - [c136]Yinpeng Dong, Hang Su, Baoyuan Wu, Zhifeng Li, Wei Liu, Tong Zhang, Jun Zhu:
Efficient Decision-Based Black-Box Adversarial Attacks on Face Recognition. CVPR 2019: 7714-7722 - [c135]Meng Fang, Cheng Zhou, Bei Shi, Boqing Gong, Jia Xu, Tong Zhang:
DHER: Hindsight Experience Replay for Dynamic Goals. ICLR (Poster) 2019 - [c134]Lei Han, Peng Sun, Yali Du, Jiechao Xiong, Qing Wang, Xinghai Sun, Han Liu, Tong Zhang:
Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI. ICML 2019: 2576-2585 - [c133]Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong:
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks. ICML 2019: 3866-3876 - [c132]Hanlin Tang, Chen Yu, Xiangru Lian, Tong Zhang, Ji Liu:
DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression. ICML 2019: 6155-6165 - [c131]Xiangyu Xi, Tong Zhang, Wei Ye, Jinglei Zhang, Rui Xie, Shikun Zhang:
A Hybrid Character Representation for Chinese Event Detection. IJCNN 2019: 1-8 - [c130]Qing Wang, Yingru Li, Jiechao Xiong, Tong Zhang:
Divergence-Augmented Policy Optimization. NeurIPS 2019: 6097-6108 - [i96]Baoyuan Wu, Weidong Chen, Yanbo Fan, Yong Zhang, Jinlong Hou, Jie Liu, Junzhou Huang, Wei Liu, Tong Zhang:
Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning. CoRR abs/1901.01703 (2019) - [i95]Cong Fang, Zhouchen Lin, Tong Zhang:
Sharp Analysis for Nonconvex SGD Escaping from Saddle Points. CoRR abs/1902.00247 (2019) - [i94]Zi-Yi Dou, Zhaopeng Tu, Xing Wang, Longyue Wang, Shuming Shi, Tong Zhang:
Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement. CoRR abs/1902.05770 (2019) - [i93]Yinpeng Dong, Hang Su, Baoyuan Wu, Zhifeng Li, Wei Liu, Tong Zhang, Jun Zhu:
Efficient Decision-based Black-box Adversarial Attacks on Face Recognition. CoRR abs/1904.04433 (2019) - [i92]Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong:
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks. CoRR abs/1905.00441 (2019) - [i91]Baoyuan Wu, Li Shen, Bernard Ghanem, Tong Zhang:
MAP Inference via L2-Sphere Linear Program Reformulation. CoRR abs/1905.03433 (2019) - [i90]Hanlin Tang, Xiangru Lian, Tong Zhang, Ji Liu:
DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression. CoRR abs/1905.05957 (2019) - [i89]Hanlin Tang, Xiangru Lian, Shuang Qiu, Lei Yuan, Ce Zhang, Tong Zhang, Ji Liu:
DeepSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression. CoRR abs/1907.07346 (2019) - [i88]Haishan Ye, Tong Zhang:
Mirror Natural Evolution Strategies. CoRR abs/1910.11490 (2019) - [i87]Cong Fang, Hanze Dong, Tong Zhang:
Over Parameterized Two-level Neural Networks Can Learn Near Optimal Feature Representations. CoRR abs/1910.11508 (2019) - [i86]Shizhe Diao, Jiaxin Bai, Yan Song, Tong Zhang, Yonggang Wang:
ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations. CoRR abs/1911.00720 (2019) - [i85]Zhichao Huang, Tong Zhang:
Black-Box Adversarial Attack with Transferable Model-based Embedding. CoRR abs/1911.07140 (2019) - [i84]Cong Fang, Yihong Gu, Weizhong Zhang, Tong Zhang:
Convex Formulation of Overparameterized Deep Neural Networks. CoRR abs/1911.07626 (2019) - [i83]Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T. Kwok, Tong Zhang:
Multi-objective Neural Architecture Search via Predictive Network Performance Optimization. CoRR abs/1911.09336 (2019) - [i82]Zheyan Shen, Peng Cui, Tong Zhang, Kun Kuang:
Stable Learning via Sample Reweighting. CoRR abs/1911.12580 (2019) - [i81]Haishan Ye, Shusen Wang, Zhihua Zhang, Tong Zhang:
Fast Generalized Matrix Regression with Applications in Machine Learning. CoRR abs/1912.12008 (2019) - 2018
- [j58]Xiangyu Xi, Tong Zhang, Wei Ye, Wen Zhao, Shikun Zhang, Dongdong Du, Qing Gao:
An Ensemble Approach for Detecting Anomalous User Behaviors. Int. J. Softw. Eng. Knowl. Eng. 28(11-12): 1637-1656 (2018) - [j57]Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang:
Near-optimal stochastic approximation for online principal component estimation. Math. Program. 167(1): 75-97 (2018) - [j56]Zhaopeng Tu, Yang Liu, Shuming Shi, Tong Zhang:
Learning to Remember Translation History with a Continuous Cache. Trans. Assoc. Comput. Linguistics 6: 407-420 (2018) - [j55]Dong Dai, Lei Han, Ting Yang, Tong Zhang:
Bayesian Model Averaging With Exponentiated Least Squares Loss. IEEE Trans. Inf. Theory 64(5): 3331-3345 (2018) - [c129]Longyue Wang, Zhaopeng Tu, Shuming Shi, Tong Zhang, Yvette Graham, Qun Liu:
Translating Pro-Drop Languages With Reconstruction Models. AAAI 2018: 4937-4945 - [c128]Yang Feng, Lin Ma, Wei Liu, Tong Zhang, Jiebo Luo:
Video Re-localization. ECCV (14) 2018: 55-70 - [c127]Xinyu Gong, Haozhi Huang, Lin Ma, Fumin Shen, Wei Liu, Tong Zhang:
Neural Stereoscopic Image Style Transfer. ECCV (5) 2018: 56-71 - [c126]Minjun Li, Haozhi Huang, Lin Ma, Wei Liu, Tong Zhang, Yu-Gang Jiang:
Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks. ECCV (9) 2018: 186-201 - [c125]Kaipeng Zhang, Zhanpeng Zhang, Chia-Wen Cheng, Winston H. Hsu, Yu Qiao, Wei Liu, Tong Zhang:
Super-Identity Convolutional Neural Network for Face Hallucination. ECCV (11) 2018: 196-211 - [c124]Yonggen Ling, Linchao Bao, Zequn Jie, Fengming Zhu, Ziyang Li, Shanmin Tang, Yongsheng Liu, Wei Liu, Tong Zhang:
Modeling Varying Camera-IMU Time Offset in Optimization-Based Visual-Inertial Odometry. ECCV (9) 2018: 491-507 - [c123]Wenhao Jiang, Lin Ma, Yu-Gang Jiang, Wei Liu, Tong Zhang:
Recurrent Fusion Network for Image Captioning. ECCV (2) 2018: 510-526 - [c122]Yitong Wang, Dihong Gong, Zheng Zhou, Xing Ji, Hao Wang, Zhifeng Li, Wei Liu, Tong Zhang:
Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition. ECCV (15) 2018: 764-779 - [c121]Jian Li, Zhaopeng Tu, Baosong Yang, Michael R. Lyu, Tong Zhang:
Multi-Head Attention with Disagreement Regularization. EMNLP 2018: 2897-2903 - [c120]Yi Liao, Lidong Bing, Piji Li, Shuming Shi, Wai Lam, Tong Zhang:
QuaSE: Sequence Editing under Quantifiable Guidance. EMNLP 2018: 3855-3864 - [c119]Zi-Yi Dou, Zhaopeng Tu, Xing Wang, Shuming Shi, Tong Zhang:
Exploiting Deep Representations for Neural Machine Translation. EMNLP 2018: 4253-4262 - [c118]Baosong Yang, Zhaopeng Tu, Derek F. Wong, Fandong Meng, Lidia S. Chao, Tong Zhang:
Modeling Localness for Self-Attention Networks. EMNLP 2018: 4449-4458 - [c117]Lei Han, Yiheng Huang, Tong Zhang:
Candidates vs. Noises Estimation for Large Multi-Class Classification Problem. ICML 2018: 1885-1894 - [c116]Rie Johnson, Tong Zhang:
Composite Functional Gradient Learning of Generative Adversarial Models. ICML 2018: 2376-2384 - [c115]Wenhan Luo, Peng Sun, Fangwei Zhong, Wei Liu, Tong Zhang, Yizhou Wang:
End-to-end Active Object Tracking via Reinforcement Learning. ICML 2018: 3292-3301 - [c114]Li Shen, Peng Sun, Yitong Wang, Wei Liu, Tong Zhang:
An Algorithmic Framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-Gradient Method. ICML 2018: 4641-4650 - [c113]Qiang Sun, Kean Ming Tan, Han Liu, Tong Zhang:
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation. ICML 2018: 4817-4824 - [c112]Jiaxiang Wu, Weidong Huang, Junzhou Huang, Tong Zhang:
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization. ICML 2018: 5321-5329 - [c111]Weizhong Zhang, Bin Hong, Lin Ma, Wei Liu, Tong Zhang:
Safe Element Screening for Submodular Function Minimization. ICML 2018: 5781-5790 - [c110]Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Basar:
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents. ICML 2018: 5867-5876 - [c109]Luo Luo, Wenpeng Zhang, Zhihua Zhang, Wenwu Zhu, Tong Zhang, Jian Pei:
Sketched Follow-The-Regularized-Leader for Online Factorization Machine. KDD 2018: 1900-1909 - [c108]Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang:
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator. NeurIPS 2018: 687-697 - [c107]Jianqiao Wangni, Jialei Wang, Ji Liu, Tong Zhang:
Gradient Sparsification for Communication-Efficient Distributed Optimization. NeurIPS 2018: 1306-1316 - [c106]Qing Wang, Jiechao Xiong, Lei Han, Peng Sun, Han Liu, Tong Zhang:
Exponentially Weighted Imitation Learning for Batched Historical Data. NeurIPS 2018: 6291-6300 - [c105]Hanlin Tang, Shaoduo Gan, Ce Zhang, Tong Zhang, Ji Liu:
Communication Compression for Decentralized Training. NeurIPS 2018: 7663-7673 - [c104]Jianfei Chen, Jun Zhu, Yee Whye Teh, Tong Zhang:
Stochastic Expectation Maximization with Variance Reduction. NeurIPS 2018: 7978-7988 - [c103]Conghui Tan, Tong Zhang, Shiqian Ma, Ji Liu:
Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity. NeurIPS 2018: 8376-8385 - [c102]Xinpeng Chen, Jingyuan Chen, Lin Ma, Jian Yao, Wei Liu, Jiebo Luo, Tong Zhang:
Fine-grained Video Attractiveness Prediction Using Multimodal Deep Learning on a Large Real-world Dataset. WWW (Companion Volume) 2018: 671-678 - [i80]Longyue Wang, Zhaopeng Tu, Shuming Shi, Tong Zhang, Yvette Graham, Qun Liu:
Translating Pro-Drop Languages with Reconstruction Models. CoRR abs/1801.03257 (2018) - [i79]Rie Johnson, Tong Zhang:
Composite Functional Gradient Learning of Generative Adversarial Models. CoRR abs/1801.06309 (2018) - [i78]Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Basar:
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents. CoRR abs/1802.08757 (2018) - [i77]Xinyu Gong, Haozhi Huang, Lin Ma, Fumin Shen, Wei Liu, Tong Zhang:
Neural Stereoscopic Image Style Transfer. CoRR abs/1802.09985 (2018) - [i76]Hanlin Tang, Ce Zhang, Shaoduo Gan, Tong Zhang, Ji Liu:
Decentralization Meets Quantization. CoRR abs/1803.06443 (2018) - [i75]Xinpeng Chen, Jingyuan Chen, Lin Ma, Jian Yao, Wei Liu, Jiebo Luo, Tong Zhang:
Fine-grained Video Attractiveness Prediction Using Multimodal Deep Learning on a Large Real-world Dataset. CoRR abs/1804.01373 (2018) - [i74]Yi Liao, Lidong Bing, Piji Li, Shuming Shi, Wai Lam, Tong Zhang:
Incorporating Pseudo-Parallel Data for Quantifiable Sequence Editing. CoRR abs/1804.07007 (2018) - [i73]Li Shen, Peng Sun, Yitong Wang, Wei Liu, Tong Zhang:
An Algorithmic Framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-Gradient Method. CoRR abs/1805.06137 (2018) - [i72]Weizhong Zhang, Bin Hong, Lin Ma, Wei Liu, Tong Zhang:
Safe Element Screening for Submodular Function Minimization. CoRR abs/1805.08527 (2018) - [i71]Jiaxiang Wu, Weidong Huang, Junzhou Huang, Tong Zhang:
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization. CoRR abs/1806.08054 (2018) - [i70]Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang:
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator. CoRR abs/1807.01695 (2018) - [i69]Minjun Li, Haozhi Huang, Lin Ma, Wei Liu, Tong Zhang, Yu-Gang Jiang:
Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks. CoRR abs/1807.08536 (2018) - [i68]Wenhao Jiang, Lin Ma, Yu-Gang Jiang, Wei Liu, Tong Zhang:
Recurrent Fusion Network for Image Captioning. CoRR abs/1807.09986 (2018) - [i67]Yang Feng, Lin Ma, Wei Liu, Tong Zhang, Jiebo Luo:
Video Re-localization. CoRR abs/1808.01575 (2018) - [i66]Wenhan Luo, Peng Sun, Fangwei Zhong, Wei Liu, Tong Zhang, Yizhou Wang:
End-to-end Active Object Tracking and Its Real-world Deployment via Reinforcement Learning. CoRR abs/1808.03405 (2018) - [i65]Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang:
Diffusion Approximations for Online Principal Component Estimation and Global Convergence. CoRR abs/1808.09645 (2018) - [i64]Kean Ming Tan, Zhaoran Wang, Tong Zhang, Han Liu, R. Dennis Cook:
A convex formulation for high-dimensional sparse sliced inverse regression. CoRR abs/1809.06024 (2018) - [i63]Peng Sun, Xinghai Sun, Lei Han, Jiechao Xiong, Qing Wang, Bo Li, Yang Zheng, Ji Liu, Yongsheng Liu, Han Liu, Tong Zhang:
TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game. CoRR abs/1809.07193 (2018) - [i62]Chao-Bing Song, Ji Liu, Han Liu, Yong Jiang, Tong Zhang:
Fully Implicit Online Learning. CoRR abs/1809.09350 (2018) - [i61]Yonggen Ling, Linchao Bao, Zequn Jie, Fengming Zhu, Ziyang Li, Shanmin Tang, Yongsheng Liu, Wei Liu, Tong Zhang:
Modeling Varying Camera-IMU Time Offset in Optimization-Based Visual-Inertial Odometry. CoRR abs/1810.05456 (2018) - [i60]Jiechao Xiong, Qing Wang, Zhuoran Yang, Peng Sun, Lei Han, Yang Zheng, Haobo Fu, Tong Zhang, Ji Liu, Han Liu:
Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space. CoRR abs/1810.06394 (2018) - [i59]Yitong Wang, Dihong Gong, Zheng Zhou, Xing Ji, Hao Wang, Zhifeng Li, Wei Liu, Tong Zhang:
Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition. CoRR abs/1810.07599 (2018) - [i58]Zi-Yi Dou, Zhaopeng Tu, Xing Wang, Shuming Shi, Tong Zhang:
Exploiting Deep Representations for Neural Machine Translation. CoRR abs/1810.10181 (2018) - [i57]Baosong Yang, Zhaopeng Tu, Derek F. Wong, Fandong Meng, Lidia S. Chao, Tong Zhang:
Modeling Localness for Self-Attention Networks. CoRR abs/1810.10182 (2018) - [i56]Jian Li, Zhaopeng Tu, Baosong Yang, Michael R. Lyu, Tong Zhang:
Multi-Head Attention with Disagreement Regularization. CoRR abs/1810.10183 (2018) - [i55]Conghui Tan, Tong Zhang, Shiqian Ma, Ji Liu:
Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity. CoRR abs/1811.01182 (2018) - [i54]Kaipeng Zhang, Zhanpeng Zhang, Chia-Wen Cheng, Winston H. Hsu, Yu Qiao, Wei Liu, Tong Zhang:
Super-Identity Convolutional Neural Network for Face Hallucination. CoRR abs/1811.02328 (2018) - [i53]Xiang Kong, Zhaopeng Tu, Shuming Shi, Eduard H. Hovy, Tong Zhang:
Neural Machine Translation with Adequacy-Oriented Learning. CoRR abs/1811.08541 (2018) - [i52]Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Basar:
Finite-Sample Analyses for Fully Decentralized Multi-Agent Reinforcement Learning. CoRR abs/1812.02783 (2018) - [i51]Haishan Ye, Zhichao Huang, Cong Fang, Chris Junchi Li, Tong Zhang:
Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack. CoRR abs/1812.11377 (2018) - 2017
- [j54]Shun Zheng, Jialei Wang, Fen Xia, Wei Xu, Tong Zhang:
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization. J. Mach. Learn. Res. 18: 115:1-115:52 (2017) - [j53]Xiao-Tong Yuan, Ping Li, Tong Zhang:
Gradient Hard Thresholding Pursuit. J. Mach. Learn. Res. 18: 166:1-166:43 (2017) - [j52]Jun Song, Jun Xiao, Fei Wu, Haishan Wu, Tong Zhang, Zhongfei (Mark) Zhang, Wenwu Zhu:
Hierarchical Contextual Attention Recurrent Neural Network for Map Query Suggestion. IEEE Trans. Knowl. Data Eng. 29(9): 1888-1901 (2017) - [j51]Jun Li, Tong Zhang, Wei Luo, Jian Yang, Xiao-Tong Yuan, Jian Zhang:
Sparseness Analysis in the Pretraining of Deep Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 28(6): 1425-1438 (2017) - [c101]Rie Johnson, Tong Zhang:
Deep Pyramid Convolutional Neural Networks for Text Categorization. ACL (1) 2017: 562-570 - [c100]Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang:
Efficient Distributed Learning with Sparsity. ICML 2017: 3636-3645 - [c99]Wenpeng Zhang, Peilin Zhao, Wenwu Zhu, Steven C. H. Hoi, Tong Zhang:
Projection-free Distributed Online Learning in Networks. ICML 2017: 4054-4062 - [c98]Chris Junchi Li, Mengdi Wang, Tong Zhang:
Diffusion Approximations for Online Principal Component Estimation and Global Convergence. NIPS 2017: 645-655 - [c97]Xingguo Li, Lin Yang, Jason Ge, Jarvis D. Haupt, Tong Zhang, Tuo Zhao:
On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning. NIPS 2017: 2742-2752 - [i50]Xingguo Li, Lin F. Yang, Jason Ge, Jarvis D. Haupt, Tong Zhang, Tuo Zhao:
On Quadratic Convergence of DC Proximal Newton Algorithm for Nonconvex Sparse Learning in High Dimensions. CoRR abs/1706.06066 (2017) - [i49]Jialei Wang, Tong Zhang:
Improved Optimization of Finite Sums with Minibatch Stochastic Variance Reduced Proximal Iterations. CoRR abs/1706.07001 (2017) - [i48]Jianqiao Wangni, Jialei Wang, Ji Liu, Tong Zhang:
Gradient Sparsification for Communication-Efficient Distributed Optimization. CoRR abs/1710.09854 (2017) - [i47]Lei Han, Tong Zhang:
Candidates v.s. Noises Estimation for Large Multi-Class Classification Problem. CoRR abs/1711.00658 (2017) - [i46]Zhaopeng Tu, Yang Liu, Shuming Shi, Tong Zhang:
Learning to Remember Translation History with a Continuous Cache. CoRR abs/1711.09367 (2017) - 2016
- [j50]Shusen Wang, Zhihua Zhang, Tong Zhang:
Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition. J. Mach. Learn. Res. 17: 210:1-210:49 (2016) - [j49]Shai Shalev-Shwartz, Tong Zhang:
Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization. Math. Program. 155(1-2): 105-145 (2016) - [c96]Rie Johnson, Tong Zhang:
Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings. ICML 2016: 526-534 - [c95]Zhuoran Yang, Zhaoran Wang, Han Liu, Yonina C. Eldar, Tong Zhang:
Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity. ICML 2016: 2472-2481 - [c94]Lei Han, Yu Zhang, Xiu-Feng Wan, Tong Zhang:
Generalized Hierarchical Sparse Model for Arbitrary-Order Interactive Antigenic Sites Identification in Flu Virus Data. KDD 2016: 865-874 - [c93]Lei Han, Yu Zhang, Tong Zhang:
Fast Component Pursuit for Large-Scale Inverse Covariance Estimation. KDD 2016: 1585-1594 - [c92]Xiao-Tong Yuan, Ping Li, Tong Zhang:
Exact Recovery of Hard Thresholding Pursuit. NIPS 2016: 3558-3566 - [c91]Xiao-Tong Yuan, Ping Li, Tong Zhang, Qingshan Liu, Guangcan Liu:
Learning Additive Exponential Family Graphical Models via \ell_{2, 1}-norm Regularized M-Estimation. NIPS 2016: 4367-4375 - [i45]Rie Johnson, Tong Zhang:
Supervised and Semi-Supervised Text Categorization using One-Hot LSTM for Region Embeddings. CoRR abs/1602.02373 (2016) - [i44]Shun Zheng, Fen Xia, Wei Xu, Tong Zhang:
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization. CoRR abs/1604.03763 (2016) - [i43]Lei Han, Ting Yang, Tong Zhang:
Local Uncertainty Sampling for Large-Scale Multi-Class Logistic Regression. CoRR abs/1604.08098 (2016) - [i42]Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang:
Efficient Distributed Learning with Sparsity. CoRR abs/1605.07991 (2016) - [i41]Rie Johnson, Tong Zhang:
Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level. CoRR abs/1609.00718 (2016) - 2015
- [b3]Sholom M. Weiss, Nitin Indurkhya, Tong Zhang:
Fundamentals of Predictive Text Mining, Second Edition. Texts in Computer Science, Springer 2015, ISBN 978-1-4471-6749-5, pp. 1-222 - [j48]Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel J. Hsu, Tong Zhang:
Learning sparse low-threshold linear classifiers. J. Mach. Learn. Res. 16: 1275-1304 (2015) - [c90]Peilin Zhao, Tong Zhang:
Stochastic Optimization with Importance Sampling for Regularized Loss Minimization. ICML 2015: 1-9 - [c89]Peilin Zhao, Jinwei Yang, Tong Zhang, Ping Li:
Adaptive Stochastic Alternating Direction Method of Multipliers. ICML 2015: 69-77 - [c88]Rie Johnson, Tong Zhang:
Effective Use of Word Order for Text Categorization with Convolutional Neural Networks. HLT-NAACL 2015: 103-112 - [c87]Zheng Qu, Peter Richtárik, Tong Zhang:
Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling. NIPS 2015: 865-873 - [c86]Rie Johnson, Tong Zhang:
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding. NIPS 2015: 919-927 - [c85]Daniel Vainsencher, Han Liu, Tong Zhang:
Local Smoothness in Variance Reduced Optimization. NIPS 2015: 2179-2187 - [c84]Tian Tian, Jun Zhu, Fen Xia, Xin Zhuang, Tong Zhang:
Crowd Fraud Detection in Internet Advertising. WWW 2015: 1100-1110 - [i40]Shusen Wang, Zhihua Zhang, Tong Zhang:
Towards More Efficient Nystrom Approximation and CUR Matrix Decomposition. CoRR abs/1503.08395 (2015) - [i39]Rie Johnson, Tong Zhang:
Semi-Supervised Learning with Multi-View Embedding: Theory and Application with Convolutional Neural Networks. CoRR abs/1504.01255 (2015) - [i38]Shusen Wang, Zhihua Zhang, Tong Zhang:
Improved Analyses of the Randomized Power Method and Block Lanczos Method. CoRR abs/1508.06429 (2015) - [i37]Zhuoran Yang, Zhaoran Wang, Han Liu, Yonina C. Eldar, Tong Zhang:
Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference. CoRR abs/1511.04514 (2015) - 2014
- [j47]Daniel J. Hsu, Sham M. Kakade, Tong Zhang:
Random Design Analysis of Ridge Regression. Found. Comput. Math. 14(3): 569-600 (2014) - [j46]Rie Johnson, Tong Zhang:
Learning Nonlinear Functions Using Regularized Greedy Forest. IEEE Trans. Pattern Anal. Mach. Intell. 36(5): 942-954 (2014) - [j45]Lin Xiao, Tong Zhang:
A Proximal Stochastic Gradient Method with Progressive Variance Reduction. SIAM J. Optim. 24(4): 2057-2075 (2014) - [j44]Xiao-Tong Yuan, Tong Zhang:
Partial Gaussian Graphical Model Estimation. IEEE Trans. Inf. Theory 60(3): 1673-1687 (2014) - [c83]Ping Li, Cun-Hui Zhang, Tong Zhang:
Compressed Counting Meets Compressed Sensing. COLT 2014: 1058-1077 - [c82]Shai Shalev-Shwartz, Tong Zhang:
Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization. ICML 2014: 64-72 - [c81]Xiaotong Yuan, Ping Li, Tong Zhang:
Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization. ICML 2014: 127-135 - [c80]Ohad Shamir, Nathan Srebro, Tong Zhang:
Communication-Efficient Distributed Optimization using an Approximate Newton-type Method. ICML 2014: 1000-1008 - [c79]Peng Sun, Tong Zhang, Jie Zhou:
A Convergence Rate Analysis for LogitBoost, MART and Their Variant. ICML 2014: 1251-1259 - [c78]Mu Li, Tong Zhang, Yuqiang Chen, Alexander J. Smola:
Efficient mini-batch training for stochastic optimization. KDD 2014: 661-670 - [c77]Chen Cheng, Fen Xia, Tong Zhang, Irwin King, Michael R. Lyu:
Gradient boosting factorization machines. RecSys 2014: 265-272 - [c76]Quanquan Gu, Tong Zhang, Jiawei Han:
Batch-Mode Active Learning via Error Bound Minimization. UAI 2014: 300-309 - [i36]Ping Li, Cun-Hui Zhang, Tong Zhang:
Sparse Recovery with Very Sparse Compressed Counting. CoRR abs/1401.0201 (2014) - [i35]Peilin Zhao, Tong Zhang:
Stochastic Optimization with Importance Sampling. CoRR abs/1401.2753 (2014) - [i34]Peilin Zhao, Tong Zhang:
Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling. CoRR abs/1405.3080 (2014) - [i33]Zheng Qu, Peter Richtárik, Tong Zhang:
Randomized Dual Coordinate Ascent with Arbitrary Sampling. CoRR abs/1411.5873 (2014) - [i32]Rie Johnson, Tong Zhang:
Effective Use of Word Order for Text Categorization with Convolutional Neural Networks. CoRR abs/1412.1058 (2014) - [i31]Tuo Zhao, Han Liu, Tong Zhang:
Pathwise Coordinate Optimization for Sparse Learning: Algorithm and Theory. CoRR abs/1412.7477 (2014) - [i30]Shusen Wang, Tong Zhang, Zhihua Zhang:
Adjusting Leverage Scores by Row Weighting: A Practical Approach to Coherent Matrix Completion. CoRR abs/1412.7938 (2014) - 2013
- [j43]Shai Shalev-Shwartz, Tong Zhang:
Stochastic dual coordinate ascent methods for regularized loss. J. Mach. Learn. Res. 14(1): 567-599 (2013) - [j42]Xiao-Tong Yuan, Tong Zhang:
Truncated power method for sparse eigenvalue problems. J. Mach. Learn. Res. 14(1): 899-925 (2013) - [j41]Lin Xiao, Tong Zhang:
A Proximal-Gradient Homotopy Method for the Sparse Least-Squares Problem. SIAM J. Optim. 23(2): 1062-1091 (2013) - [c75]Ohad Shamir, Tong Zhang:
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes. ICML (1) 2013: 71-79 - [c74]Rie Johnson, Tong Zhang:
Accelerating Stochastic Gradient Descent using Predictive Variance Reduction. NIPS 2013: 315-323 - [c73]Shai Shalev-Shwartz, Tong Zhang:
Accelerated Mini-Batch Stochastic Dual Coordinate Ascent. NIPS 2013: 378-385 - [c72]Krishnakumar Balasubramanian, Kai Yu, Tong Zhang:
High-dimensional Joint Sparsity Random Effects Model for Multi-task Learning. UAI 2013 - [i29]Shai Shalev-Shwartz, Tong Zhang:
Accelerated Mini-Batch Stochastic Dual Coordinate Ascent. CoRR abs/1305.2581 (2013) - [i28]Shai Shalev-Shwartz, Tong Zhang:
Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization. CoRR abs/1309.2375 (2013) - [i27]Krishnakumar Balasubramanian, Kai Yu, Tong Zhang:
High-dimensional Joint Sparsity Random Effects Model for Multi-task Learning. CoRR abs/1309.6814 (2013) - [i26]Ping Li, Cun-Hui Zhang, Tong Zhang:
Compressed Counting Meets Compressed Sensing. CoRR abs/1310.1076 (2013) - [i25]Dong Dai, Philippe Rigollet, Lucy Xia, Tong Zhang:
Aggregation of Affine Estimators. CoRR abs/1311.2799 (2013) - [i24]Xiao-Tong Yuan, Ping Li, Tong Zhang:
Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization. CoRR abs/1311.5750 (2013) - [i23]Peilin Zhao, Jinwei Yang, Tong Zhang, Ping Li:
Accelerating Stochastic Alternating Direction Method of Multipliers with Adaptive Subgradient. CoRR abs/1312.4564 (2013) - [i22]Ohad Shamir, Nathan Srebro, Tong Zhang:
Communication Efficient Distributed Optimization using an Approximate Newton-type Method. CoRR abs/1312.7853 (2013) - 2012
- [j40]J. Lamar Barnett, Jialiang Yang, Zhipeng Cai, Tong Zhang, Xiu-Feng Wan:
AntigenMap 3D: an online antigenic cartography resource. Bioinform. 28(9): 1292-1293 (2012) - [j39]Daniel J. Hsu, Sham M. Kakade, Tong Zhang:
A spectral algorithm for learning Hidden Markov Models. J. Comput. Syst. Sci. 78(5): 1460-1480 (2012) - [c71]Lin Xiao, Tong Zhang:
A Proximal-Gradient Homotopy Method for the L1-Regularized Least-Squares Problem. ICML 2012 - [c70]Quanquan Gu, Tong Zhang, Chris H. Q. Ding, Jiawei Han:
Selective Labeling via Error Bound Minimization. NIPS 2012: 332-340 - [c69]Daniel J. Hsu, Sham M. Kakade, Tong Zhang:
Random Design Analysis of Ridge Regression. COLT 2012: 9.1-9.24 - [i21]Dong Dai, Philippe Rigollet, Tong Zhang:
Deviation Optimal Learning using Greedy Q-aggregation. CoRR abs/1203.2507 (2012) - [i20]Lin Xiao, Tong Zhang:
A Proximal-Gradient Homotopy Method for the Sparse Least-Squares Problem. CoRR abs/1203.3002 (2012) - [i19]Shai Shalev-Shwartz, Tong Zhang:
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization. CoRR abs/1209.1873 (2012) - [i18]Xiao-Tong Yuan, Tong Zhang:
Partial Gaussian Graphical Model Estimation. CoRR abs/1209.6419 (2012) - [i17]Shai Shalev-Shwartz, Tong Zhang:
Proximal Stochastic Dual Coordinate Ascent. CoRR abs/1211.2717 (2012) - [i16]Daniel J. Hsu, Sham M. Kakade, Tong Zhang:
Analysis of a randomized approximation scheme for matrix multiplication. CoRR abs/1211.5414 (2012) - [i15]Ohad Shamir, Tong Zhang:
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes. CoRR abs/1212.1824 (2012) - [i14]Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel J. Hsu, Tong Zhang:
Learning Sparse Low-Threshold Linear Classifiers. CoRR abs/1212.3276 (2012) - 2011
- [j38]Junzhou Huang, Tong Zhang, Dimitris N. Metaxas:
Learning with Structured Sparsity. J. Mach. Learn. Res. 12: 3371-3412 (2011) - [j37]Wenyuan Li, Chun-Chi Liu, Tong Zhang, Haifeng Li, Michael S. Waterman, Xianghong Jasmine Zhou:
Integrative Analysis of Many Weighted Co-Expression Networks Using Tensor Computation. PLoS Comput. Biol. 7(6) (2011) - [j36]Tong Zhang:
Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations. IEEE Trans. Inf. Theory 57(7): 4689-4708 (2011) - [j35]Tong Zhang:
Sparse Recovery With Orthogonal Matching Pursuit Under RIP. IEEE Trans. Inf. Theory 57(9): 6215-6221 (2011) - [j34]Daniel J. Hsu, Sham M. Kakade, Tong Zhang:
Robust Matrix Decomposition With Sparse Corruptions. IEEE Trans. Inf. Theory 57(11): 7221-7234 (2011) - [c68]Dong Dai, Tong Zhang:
Greedy Model Averaging. NIPS 2011: 1242-1250 - [c67]Zhen Li, Huazhong Ning, Liangliang Cao, Tong Zhang, Yihong Gong, Thomas S. Huang:
Learning to Search Efficiently in High Dimensions. NIPS 2011: 1710-1718 - [c66]Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang:
Spectral Methods for Learning Multivariate Latent Tree Structure. NIPS 2011: 2025-2033 - [c65]Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang:
Efficient Optimal Learning for Contextual Bandits. UAI 2011: 169-178 - [i13]Daniel J. Hsu, Sham M. Kakade, Tong Zhang:
Dimension-free tail inequalities for sums of random matrices. CoRR abs/1104.1672 (2011) - [i12]Daniel J. Hsu, Sham M. Kakade, Tong Zhang:
An Analysis of Random Design Linear Regression. CoRR abs/1106.2363 (2011) - [i11]Miroslav Dudík, Daniel J. Hsu, Satyen Kale, Nikos Karampatziakis, John Langford, Lev Reyzin, Tong Zhang:
Efficient Optimal Learning for Contextual Bandits. CoRR abs/1106.2369 (2011) - [i10]Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang:
Spectral Methods for Learning Multivariate Latent Tree Structure. CoRR abs/1107.1283 (2011) - [i9]Daniel J. Hsu, Sham M. Kakade, Tong Zhang:
A tail inequality for quadratic forms of subgaussian random vectors. CoRR abs/1110.2842 (2011) - [i8]Xiao-Tong Yuan, Tong Zhang:
Truncated Power Method for Sparse Eigenvalue Problems. CoRR abs/1112.2679 (2011) - 2010
- [b2]Sholom M. Weiss, Nitin Indurkhya, Tong Zhang:
Fundamentals of Predictive Text Mining. Texts in Computer Science 41, Springer 2010, ISBN 978-1-84996-225-4, pp. I-XII, 1-226 - [j33]Tong Zhang:
Analysis of Multi-stage Convex Relaxation for Sparse Regularization. J. Mach. Learn. Res. 11: 1081-1107 (2010) - [j32]Zhipeng Cai, Tong Zhang, Xiu-Feng Wan:
A Computational Framework for Influenza Antigenic Cartography. PLoS Comput. Biol. 6(10) (2010) - [j31]Shai Shalev-Shwartz, Nathan Srebro, Tong Zhang:
Trading Accuracy for Sparsity in Optimization Problems with Sparsity Constraints. SIAM J. Optim. 20(6): 2807-2832 (2010) - [c64]Xi Zhou, Kai Yu, Tong Zhang, Thomas S. Huang:
Image Classification Using Super-Vector Coding of Local Image Descriptors. ECCV (5) 2010: 141-154 - [c63]Kai Yu, Tong Zhang:
Improved Local Coordinate Coding using Local Tangents. ICML 2010: 1215-1222 - [c62]Alina Beygelzimer, Daniel J. Hsu, John Langford, Tong Zhang:
Agnostic Active Learning Without Constraints. NIPS 2010: 199-207 - [c61]Yuanqing Lin, Tong Zhang, Shenghuo Zhu, Kai Yu:
Deep Coding Network. NIPS 2010: 1405-1413 - [r1]Tong Zhang:
Fundamental Statistical Techniques. Handbook of Natural Language Processing 2010: 189-204 - [i7]Tong Zhang:
Sparse Recovery with Orthogonal Matching Pursuit under RIP. CoRR abs/1005.2249 (2010) - [i6]Alina Beygelzimer, Daniel J. Hsu, John Langford, Tong Zhang:
Agnostic Active Learning Without Constraints. CoRR abs/1006.2588 (2010) - [i5]Daniel J. Hsu, Sham M. Kakade, Tong Zhang:
Robust Matrix Decomposition with Outliers. CoRR abs/1011.1518 (2010)
2000 – 2009
- 2009
- [j30]Tong Zhang:
On the Consistency of Feature Selection using Greedy Least Squares Regression. J. Mach. Learn. Res. 10: 555-568 (2009) - [j29]John Langford, Lihong Li, Tong Zhang:
Sparse Online Learning via Truncated Gradient. J. Mach. Learn. Res. 10: 777-801 (2009) - [j28]Evgeniy Gabrilovich, Andrei Z. Broder, Marcus Fontoura, Amruta Joshi, Vanja Josifovski, Lance Riedel, Tong Zhang:
Classifying search queries using the Web as a source of knowledge. ACM Trans. Web 3(2): 5:1-5:28 (2009) - [c60]Daniel J. Hsu, Sham M. Kakade, Tong Zhang:
A Spectral Algorithm for Learning Hidden Markov Models. COLT 2009 - [c59]Junzhou Huang, Tong Zhang, Dimitris N. Metaxas:
Learning with structured sparsity. ICML 2009: 417-424 - [c58]John Langford, Ruslan Salakhutdinov, Tong Zhang:
Learning nonlinear dynamic models. ICML 2009: 593-600 - [c57]Daniel J. Hsu, Sham M. Kakade, John Langford, Tong Zhang:
Multi-Label Prediction via Compressed Sensing. NIPS 2009: 772-780 - [c56]Kai Yu, Tong Zhang, Yihong Gong:
Nonlinear Learning using Local Coordinate Coding. NIPS 2009: 2223-2231 - [i4]Daniel J. Hsu, Sham M. Kakade, John Langford, Tong Zhang:
Multi-Label Prediction via Compressed Sensing. CoRR abs/0902.1284 (2009) - [i3]John Langford, Ruslan Salakhutdinov, Tong Zhang:
Learning Nonlinear Dynamic Models. CoRR abs/0905.3369 (2009) - 2008
- [j27]Christoph Tillmann, Tong Zhang:
An Online Relevant Set Algorithm for Statistical Machine Translation. IEEE Trans. Speech Audio Process. 16(7): 1274-1286 (2008) - [j26]Rie Johnson, Tong Zhang:
Graph-Based Semi-Supervised Learning and Spectral Kernel Design. IEEE Trans. Inf. Theory 54(1): 275-288 (2008) - [j25]David Cossock, Tong Zhang:
Statistical Analysis of Bayes Optimal Subset Ranking. IEEE Trans. Inf. Theory 54(11): 5140-5154 (2008) - [c55]John Langford, Lihong Li, Tong Zhang:
Sparse Online Learning via Truncated Gradient. NIPS 2008: 905-912 - [c54]Tong Zhang:
Adaptive Forward-Backward Greedy Algorithm for Sparse Learning with Linear Models. NIPS 2008: 1921-1928 - [c53]Tong Zhang:
Multi-stage Convex Relaxation for Learning with Sparse Regularization. NIPS 2008: 1929-1936 - [e1]Rocco A. Servedio, Tong Zhang:
21st Annual Conference on Learning Theory - COLT 2008, Helsinki, Finland, July 9-12, 2008. Omnipress 2008 [contents] - [i2]John Langford, Lihong Li, Tong Zhang:
Sparse Online Learning via Truncated Gradient. CoRR abs/0806.4686 (2008) - [i1]Daniel J. Hsu, Sham M. Kakade, Tong Zhang:
A Spectral Algorithm for Learning Hidden Markov Models. CoRR abs/0811.4413 (2008) - 2007
- [j24]Rie Johnson, Tong Zhang:
On the Effectiveness of Laplacian Normalization for Graph Semi-supervised Learning. J. Mach. Learn. Res. 8: 1489-1517 (2007) - [j23]Christoph Tillmann, Tong Zhang:
A block bigram prediction model for statistical machine translation. ACM Trans. Speech Lang. Process. 4(3): 6 (2007) - [c52]Maria-Florina Balcan, Andrei Z. Broder, Tong Zhang:
Margin Based Active Learning. COLT 2007: 35-50 - [c51]Rie Kubota Ando, Tong Zhang:
Two-view feature generation model for semi-supervised learning. ICML 2007: 25-32 - [c50]John Langford, Tong Zhang:
The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information. NIPS 2007: 817-824 - [c49]Zhaohui Zheng, Hongyuan Zha, Tong Zhang, Olivier Chapelle, Keke Chen, Gordon Sun:
A General Boosting Method and its Application to Learning Ranking Functions for Web Search. NIPS 2007: 1697-1704 - [c48]Andrei Z. Broder, Marcus Fontoura, Evgeniy Gabrilovich, Amruta Joshi, Vanja Josifovski, Tong Zhang:
Robust classification of rare queries using web knowledge. SIGIR 2007: 231-238 - 2006
- [j22]Tong Zhang:
Information-theoretic upper and lower bounds for statistical estimation. IEEE Trans. Inf. Theory 52(4): 1307-1321 (2006) - [c47]Christoph Tillmann, Tong Zhang:
A Discriminative Global Training Algorithm for Statistical MT. ACL 2006 - [c46]Zixiu Guo, John D'Ambra, Tim Turner, Huiying Zhang, Tong Zhang:
Effectiveness of Meeting Outcomes in Virtual vs. Face-to-Face Teams: A Comparison Study in China. AMCIS 2006: 195 - [c45]David Cossock, Tong Zhang:
Subset Ranking Using Regression. COLT 2006: 605-619 - [c44]Tong Zhang, Alexandrin Popescul, Byron Dom:
Linear prediction models with graph regularization for web-page categorization. KDD 2006: 821-826 - [c43]Rie Kubota Ando, Tong Zhang:
Learning on Graph with Laplacian Regularization. NIPS 2006: 25-32 - 2005
- [j21]Rie Kubota Ando, Tong Zhang:
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data. J. Mach. Learn. Res. 6: 1817-1853 (2005) - [j20]Tong Zhang:
Learning Bounds for Kernel Regression Using Effective Data Dimensionality. Neural Comput. 17(9): 2077-2098 (2005) - [c42]Rie Kubota Ando, Tong Zhang:
A High-Performance Semi-Supervised Learning Method for Text Chunking. ACL 2005: 1-9 - [c41]Christoph Tillmann, Tong Zhang:
A Localized Prediction Model for Statistical Machine Translation. ACL 2005: 557-564 - [c40]Tong Zhang:
Data Dependent Concentration Bounds for Sequential Prediction Algorithms. COLT 2005: 173-187 - [c39]Tong Zhang:
Localized Upper and Lower Bounds for Some Estimation Problems. COLT 2005: 516-530 - [c38]Tong Zhang, Rie Kubota Ando:
Analysis of Spectral Kernel Design based Semi-supervised Learning. NIPS 2005: 1601-1608 - [c37]Rie Kubota Ando, Mark Dredze, Tong Zhang:
TREC 2005 Genomics Track Experiments at IBM Watson. TREC 2005 - 2004
- [j19]Fred Damerau, Tong Zhang, Sholom M. Weiss, Nitin Indurkhya:
Text categorization for a comprehensive time-dependent benchmark. Inf. Process. Manag. 40(2): 209-221 (2004) - [j18]Tong Zhang:
Statistical Analysis of Some Multi-Category Large Margin Classification Methods. J. Mach. Learn. Res. 5: 1225-1251 (2004) - [c36]Tong Zhang:
On the Convergence of MDL Density Estimation. COLT 2004: 315-330 - [c35]Tong Zhang:
Solving large scale linear prediction problems using stochastic gradient descent algorithms. ICML 2004 - [c34]Honglei Guo, Jianmin Jiang, Gang Hu, Tong Zhang:
Chinese Named Entity Recognition Based on Multilevel Linguistic Features. IJCNLP 2004: 90-99 - [c33]Jinbo Bi, Tong Zhang, Kristin P. Bennett:
Column-generation boosting methods for mixture of kernels. KDD 2004: 521-526 - [c32]Jinbo Bi, Tong Zhang:
Support Vector Classification with Input Data Uncertainty. NIPS 2004: 161-168 - [c31]Tong Zhang:
Class-size Independent Generalization Analsysis of Some Discriminative Multi-Category Classification. NIPS 2004: 1625-1632 - [c30]Li Zhang, Yue Pan, Tong Zhang:
Focused named entity recognition using machine learning. SIGIR 2004: 281-288 - 2003
- [j17]Shie Mannor, Ron Meir, Tong Zhang:
Greedy Algorithms for Classification -- Consistency, Convergence Rates, and Adaptivity. J. Mach. Learn. Res. 4: 713-741 (2003) - [j16]Ron Meir, Tong Zhang:
Generalization Error Bounds for Bayesian Mixture Algorithms. J. Mach. Learn. Res. 4: 839-860 (2003) - [j15]Tong Zhang:
Leave-One-Out Bounds for Kernel Methods. Neural Comput. 15(6): 1397-1437 (2003) - [j14]Tong Zhang:
Sequential greedy approximation for certain convex optimization problems. IEEE Trans. Inf. Theory 49(3): 682-691 (2003) - [c29]Tong Zhang, Fred Damerau, David Johnson:
Updating an NLP system to fit new domains: an empirical study on the sentence segmentation problem. CoNLL 2003: 56-62 - [c28]Radu Florian, Abraham Ittycheriah, Hongyan Jing, Tong Zhang:
Named Entity Recognition through Classifier Combination. CoNLL 2003: 168-171 - [c27]Tong Zhang, David Johnson:
A Robust Risk Minimization based Named Entity Recognition System. CoNLL 2003: 204-207 - [c26]Hongyan Jing, Radu Florian, Xiaoqiang Luo, Tong Zhang, Abraham Ittycheriah:
HowtogetaChineseName(Entity): Segmentation and Combination Issues. EMNLP 2003 - [c25]Tong Zhang, Bin Yu:
On the Convergence of Boosting Procedures. ICML 2003: 904-911 - [c24]Tong Zhang:
An Infinity-sample Theory for Multi-category Large Margin Classification. NIPS 2003: 1077-1084 - [c23]Tong Zhang:
Learning Bounds for a Generalized Family of Bayesian Posterior Distributions. NIPS 2003: 1149-1156 - 2002
- [j13]David E. Johnson, Frank J. Oles, Tong Zhang, Thilo Götz:
A decision-tree-based symbolic rule induction system for text categorization. IBM Syst. J. 41(3): 428-437 (2002) - [j12]Tong Zhang, Carlo Tomasi:
On the Consistency of Instantaneous Rigid Motion Estimation. Int. J. Comput. Vis. 46(1): 51-79 (2002) - [j11]Tong Zhang, Vijay S. Iyengar:
Recommender Systems Using Linear Classifier. J. Mach. Learn. Res. 2: 313-334 (2002) - [j10]Tong Zhang:
Covering Number Bounds of Certain Regularized Linear Function Classes. J. Mach. Learn. Res. 2: 527-550 (2002) - [j9]Tong Zhang, Fred Damerau, David Johnson:
Text Chunking based on a Generalization of Winnow. J. Mach. Learn. Res. 2: 615-637 (2002) - [j8]Tong Zhang:
On the Dual Formulation of Regularized Linear Systems with Convex Risks. Mach. Learn. 46(1-3): 91-129 (2002) - [j7]Tong Zhang:
Approximation Bounds for Some Sparse Kernel Regression Algorithms. Neural Comput. 14(12): 3013-3042 (2002) - [j6]Jane Cullum, Tong Zhang:
Two-Sided Arnoldi and Nonsymmetric Lanczos Algorithms. SIAM J. Matrix Anal. Appl. 24(2): 303-319 (2002) - [c22]Shie Mannor, Ron Meir, Tong Zhang:
The Consistency of Greedy Algorithms for Classification. COLT 2002: 319-333 - [c21]Tong Zhang:
Statistical Behavior and Consistency of Support Vector Machines, Boosting, and Beyond. ICML 2002: 690-700 - [c20]Ron Meir, Tong Zhang:
Data-Dependent Bounds for Bayesian Mixture Methods. NIPS 2002: 319-326 - [c19]Tong Zhang:
Effective Dimension and Generalization of Kernel Learning. NIPS 2002: 454-461 - [c18]Fred Damerau, Tong Zhang, Sholom M. Weiss, Nitin Indurkhya:
Experiments in high-dimensional text categorization. SIGIR 2002: 357-358 - 2001
- [j5]Tong Zhang:
An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. AI Mag. 22(2): 103-104 (2001) - [j4]Tong Zhang, Frank J. Oles:
Text Categorization Based on Regularized Linear Classification Methods. Inf. Retr. 4(1): 5-31 (2001) - [j3]Tong Zhang, Gene H. Golub:
Rank-One Approximation to High Order Tensors. SIAM J. Matrix Anal. Appl. 23(2): 534-550 (2001) - [c17]Tong Zhang, Fred Damerau, David Johnson:
Text Chunking using Regularized Winnow. ACL 2001: 539-546 - [c16]Tong Zhang:
A Sequential Approximation Bound for Some Sample-Dependent Convex Optimization Problems with Applications in Learning. COLT/EuroCOLT 2001: 65-81 - [c15]Tong Zhang:
A Leave-One-out Cross Validation Bound for Kernel Methods with Applications in Learning. COLT/EuroCOLT 2001: 427-443 - [c14]Tong Zhang:
Some Sparse Approximation Bounds for Regression Problems. ICML 2001: 624-631 - [c13]Tong Zhang:
Generalization Performance of Some Learning Problems in Hilbert Functional Spaces. NIPS 2001: 543-550 - [c12]Tong Zhang:
A General Greedy Approximation Algorithm with Applications. NIPS 2001: 1065-1072 - [c11]Vijay S. Iyengar, Tong Zhang:
Empirical Study of Recommender Systems Using Linear Classifiers. PAKDD 2001: 16-27 - 2000
- [c10]Vijay S. Iyengar, Chidanand Apté, Tong Zhang:
Active learning using adaptive resampling. KDD 2000: 91-98 - [c9]Tong Zhang:
Convergence of Large Margin Separable Linear Classification. NIPS 2000: 357-363 - [c8]Tong Zhang:
Regularized Winnow Methods. NIPS 2000: 703-709
1990 – 1999
- 1999
- [c7]Tong Zhang:
Theoretical Analysis of a Class of Randomized Regularization Methods. COLT 1999: 156-163 - [c6]Tong Zhang, Carlo Tomasi:
Fast, Robust, and Consistent Camera Motion Estimation. CVPR 1999: 1164-1170 - [c5]Tong Zhang:
Some Theoretical Results Concerning the Convergence of Compositions of Regularized Linear Functions. NIPS 1999: 370-378 - 1998
- [b1]Tong Zhang:
Methods for computational and statistical estimation with applications. Stanford University, USA, 1998 - [j2]Tong Zhang, Kincho H. Law, Gene H. Golub:
On the Homotopy Method for Perturbed Symmetric Generalized Eigenvalue Problems. SIAM J. Sci. Comput. 19(5): 1625-1645 (1998) - [c4]Tong Zhang:
Compression by Model Combination. Data Compression Conference 1998: 319-328 - [c3]Daniel H. Greene, F. Frances Yao, Tong Zhang:
A Linear Algorithm for Optimal Context Clustering with Application to Bi-level Image Coding. ICIP (1) 1998: 508-511 - 1997
- [c2]Daniel Greene, Mohan Vishwanath, F. Frances Yao, Tong Zhang:
A progressive Ziv-Lempel algorithm for image compression. SEQUENCES 1997: 136-144 - 1996
- [c1]Gabriel Taubin, Tong Zhang, Gene H. Golub:
Optimal Surface Smoothing as Filter Design. ECCV (1) 1996: 283-292 - 1995
- [j1]Robert S. Strichartz, Arthur Taylor, Tong Zhang:
Densities of Self-Similar Measures on the Line. Exp. Math. 4(2): 101-128 (1995)
Coauthor Index
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Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
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OpenAlex data
Load additional information about publications from 
.
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last updated on 2024-12-17 21:50 CET by the dblp team
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