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2020 – today
- 2024
[j26]Hao Liu, Haizhao Yang, Minshuo Chen, Tuo Zhao, Wenjing Liao:
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces. J. Mach. Learn. Res. 25: 24:1-24:67 (2024)- [j25]Zhenghao Xu, Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao:
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds. J. Mach. Learn. Res. 25: 226:1-226:67 (2024) - [j24]Yan Li
, Guanghui Lan, Tuo Zhao:
Homotopic policy mirror descent: policy convergence, algorithmic regularization, and improved sample complexity. Math. Program. 207(1): 457-513 (2024) - [j23]Tingting Zhao, Guixi Li, Tuo Zhao, Yarui Chen, Ning Xie, Gang Niu, Masashi Sugiyama:
Learning explainable task-relevant state representation for model-free deep reinforcement learning. Neural Networks 180: 106741 (2024) - [c111]Haoyu Wang, Tianci Liu, Ruirui Li, Monica Xiao Cheng, Tuo Zhao, Jing Gao:
RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuning. EMNLP 2024: 996-1008 - [c110]Haoyu Wang, Ruirui Li, Haoming Jiang, Jinjin Tian, Zhengyang Wang, Chen Luo, Xianfeng Tang, Monica Xiao Cheng, Tuo Zhao, Jing Gao:
BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering. EMNLP 2024: 1009-1025 - [c109]Alexander Bukharin, Shiyang Li, Zhengyang Wang, Jingfeng Yang, Bing Yin, Xian Li, Chao Zhang, Tuo Zhao, Haoming Jiang:
Data Diversity Matters for Robust Instruction Tuning. EMNLP (Findings) 2024: 3411-3425 - [c108]Tuo Zhao, Xinxue Wang, Tingting Zhao, Yuan Wang, Yarui Chen, Jucheng Yang:
Hybrid Deep Generative and Sequential Learning Approach for Stock Market Prediction. ICIC (LNAI 5) 2024: 263-274 - [c107]Yixiao Li, Yifan Yu, Chen Liang, Nikos Karampatziakis, Pengcheng He, Weizhu Chen, Tuo Zhao:
LoftQ: LoRA-Fine-Tuning-aware Quantization for Large Language Models. ICLR 2024 - [c106]Qingru Zhang, Chandan Singh, Liyuan Liu, Xiaodong Liu, Bin Yu, Jianfeng Gao, Tuo Zhao:
Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs. ICLR 2024 - [c105]Zichong Li, Qunzhi Xu, Zhenghao Xu, Yajun Mei, Tuo Zhao, Hongyuan Zha:
Beyond Point Prediction: Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process. ICML 2024 - [c104]Zi-Hao Qiu, Siqi Guo, Mao Xu, Tuo Zhao, Lijun Zhang, Tianbao Yang:
To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO. ICML 2024 - [c103]Zhen Shao, Tuo Zhao, Lin Zhang, Yu Liu:
Trust in Digital Commerce: the Moderating Effect of Blockchain Teability Labels. PACIS 2024 - [i120]Haoyu Wang, Tuo Zhao, Jing Gao:
BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filtering. CoRR abs/2402.11129 (2024) - [i119]Hao Kang, Qingru Zhang, Souvik Kundu, Geonhwa Jeong, Zaoxing Liu, Tushar Krishna, Tuo Zhao:
GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLM. CoRR abs/2403.05527 (2024) - [i118]Hoang Huy Nguyen, Yan Li, Tuo Zhao:
Stochastic Constrained Decentralized Optimization for Machine Learning with Fewer Data Oracles: a Gradient Sliding Approach. CoRR abs/2404.02511 (2024) - [i117]Zi-Hao Qiu, Siqi Guo, Mao Xu, Tuo Zhao, Lijun Zhang, Tianbao Yang:
To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO. CoRR abs/2404.04575 (2024) - [i116]Ilgee Hong, Zichong Li, Alexander Bukharin, Yixiao Li, Haoming Jiang, Tianbao Yang, Tuo Zhao:
Adaptive Preference Scaling for Reinforcement Learning with Human Feedback. CoRR abs/2406.02764 (2024) - [i115]Haoyu Wang, Tianci Liu, Tuo Zhao, Jing Gao:
RoseLoRA: Row and Column-wise Sparse Low-rank Adaptation of Pre-trained Language Model for Knowledge Editing and Fine-tuning. CoRR abs/2406.10777 (2024) - [i114]Alexander Bukharin, Ilgee Hong, Haoming Jiang, Qingru Zhang, Zixuan Zhang, Tuo Zhao:
Robust Reinforcement Learning from Corrupted Human Feedback. CoRR abs/2406.15568 (2024) - [i113]Qingru Zhang, Xiaodong Yu, Chandan Singh, Xiaodong Liu, Liyuan Liu, Jianfeng Gao, Tuo Zhao, Dan Roth, Hao Cheng:
Model Tells Itself Where to Attend: Faithfulness Meets Automatic Attention Steering. CoRR abs/2409.10790 (2024) - [i112]Kuan Wang, Alexander Bukharin, Haoming Jiang, Qingyu Yin, Zhengyang Wang, Tuo Zhao, Jingbo Shang, Chao Zhang, Bing Yin, Xian Li, Jianshu Chen, Shiyang Li:
RNR: Teaching Large Language Models to Follow Roles and Rules. CoRR abs/2409.13733 (2024) - [i111]Zhenghao Xu, Yuqing Wang, Tuo Zhao, Rachel Ward, Molei Tao:
Provable Acceleration of Nesterov's Accelerated Gradient for Rectangular Matrix Factorization and Linear Neural Networks. CoRR abs/2410.09640 (2024) - 2023
- [j22]Ethan X. Fang, Yajun Mei, Yuyang Shi, Qunzhi Xu, Tuo Zhao:
Pivotal Estimation of Linear Discriminant Analysis in High Dimensions. J. Mach. Learn. Res. 24: 302:1-302:45 (2023) - [j21]Guanghui Lan, Yan Li, Tuo Zhao:
Block Policy Mirror Descent. SIAM J. Optim. 33(3): 2341-2378 (2023) - [c102]Simiao Zuo, Qingyu Yin, Haoming Jiang, Shaohui Xi, Bing Yin, Chao Zhang, Tuo Zhao:
Context-Aware Query Rewriting for Improving Users' Search Experience on E-commerce Websites. ACL (industry) 2023: 616-628 - [c101]Jiachen Yang, Tarik Dzanic, Brenden K. Petersen, Jun Kudo, Ketan Mittal, Vladimir Z. Tomov, Jean-Sylvain Camier, Tuo Zhao, Hongyuan Zha, Tzanio V. Kolev, Robert W. Anderson, Daniel M. Faissol:
Reinforcement Learning for Adaptive Mesh Refinement. AISTATS 2023: 5997-6014 - [c100]Qingru Zhang, Dhananjay Ram, Cole Hawkins, Sheng Zha, Tuo Zhao:
Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer. EMNLP (Findings) 2023: 2775-2786 - [c99]Haoyu Wang, Yaqing Wang, Tianci Liu, Tuo Zhao, Jing Gao:
HadSkip: Homotopic and Adaptive Layer Skipping of Pre-trained Language Models for Efficient Inference. EMNLP (Findings) 2023: 4283-4294 - [c98]Suliang Bu, Tuo Zhao, Yunxin Zhao:
Joint Estimation of DOA and Distance in Noisy Reverberant Conditions. ICASSP 2023: 1-5 - [c97]Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao:
Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks. ICLR 2023 - [c96]Chen Liang, Haoming Jiang, Zheng Li, Xianfeng Tang, Bing Yin, Tuo Zhao:
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers. ICLR 2023 - [c95]Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, Tuo Zhao:
Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning. ICLR 2023 - [c94]Alexander Bukharin, Tianyi Liu, Shengjie Wang, Simiao Zuo, Weihao Gao, Wen Yan, Tuo Zhao:
Machine Learning Force Fields with Data Cost Aware Training. ICML 2023: 3219-3232 - [c93]Minshuo Chen, Kaixuan Huang, Tuo Zhao, Mengdi Wang:
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data. ICML 2023: 4672-4712 - [c92]Zichong Li, Yanbo Xu, Simiao Zuo, Haoming Jiang, Chao Zhang, Tuo Zhao, Hongyuan Zha:
SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process. ICML 2023: 20210-20220 - [c91]Yixiao Li, Yifan Yu, Qingru Zhang, Chen Liang, Pengcheng He, Weizhu Chen, Tuo Zhao:
LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation. ICML 2023: 20336-20350 - [c90]Chen Liang, Simiao Zuo, Qingru Zhang, Pengcheng He, Weizhu Chen, Tuo Zhao:
Less is More: Task-aware Layer-wise Distillation for Language Model Compression. ICML 2023: 20852-20867 - [c89]Zixuan Zhang, Minshuo Chen, Mengdi Wang, Wenjing Liao, Tuo Zhao:
Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories. ICML 2023: 40911-40931 - [c88]Haoyu Wang, Ruirui Li, Haoming Jiang, Zhengyang Wang, Xianfeng Tang, Bin Bi, Monica Xiao Cheng, Bing Yin, Yaqing Wang, Tuo Zhao, Jing Gao:
LightToken: A Task and Model-agnostic Lightweight Token Embedding Framework for Pre-trained Language Models. KDD 2023: 2302-2313 - [c87]Chen Liang, Jiahui Yu, Ming-Hsuan Yang, Matthew Brown, Yin Cui, Tuo Zhao, Boqing Gong, Tianyi Zhou:
Module-wise Adaptive Distillation for Multimodality Foundation Models. NeurIPS 2023 - [c86]Alexander Bukharin, Yan Li, Yue Yu, Qingru Zhang, Zhehui Chen, Simiao Zuo, Chao Zhang, Songan Zhang, Tuo Zhao:
Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms. NeurIPS 2023 - [c85]Shenao Zhang, Boyi Liu, Zhaoran Wang, Tuo Zhao:
Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms. NeurIPS 2023 - [i110]Minshuo Chen, Kaixuan Huang, Tuo Zhao, Mengdi Wang:
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data. CoRR abs/2302.07194 (2023) - [i109]Chen Liang, Haoming Jiang, Zheng Li, Xianfeng Tang, Bin Yin, Tuo Zhao:
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers. CoRR abs/2302.09632 (2023) - [i108]Biraj Dahal, Alexander Havrilla, Minshuo Chen, Tuo Zhao, Wenjing Liao:
On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds. CoRR abs/2302.13183 (2023) - [i107]Qingru Zhang, Minshuo Chen, Alexander Bukharin, Pengcheng He, Yu Cheng, Weizhu Chen, Tuo Zhao:
Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning. CoRR abs/2303.10512 (2023) - [i106]Alexander Bukharin, Tianyi Liu, Shengjie Wang, Simiao Zuo, Weihao Gao, Wen Yan, Tuo Zhao:
Machine Learning Force Fields with Data Cost Aware Training. CoRR abs/2306.03109 (2023) - [i105]Yixiao Li, Yifan Yu, Qingru Zhang, Chen Liang, Pengcheng He, Weizhu Chen, Tuo Zhao:
LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation. CoRR abs/2306.11222 (2023) - [i104]Zixuan Zhang, Minshuo Chen, Mengdi Wang, Wenjing Liao, Tuo Zhao:
Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories. CoRR abs/2306.14859 (2023) - [i103]Kaiqi Zhang, Zixuan Zhang, Minshuo Chen, Mengdi Wang, Tuo Zhao, Yu-Xiang Wang:
Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks. CoRR abs/2307.01649 (2023) - [i102]Xiang Ji, Huazheng Wang, Minshuo Chen, Tuo Zhao, Mengdi Wang:
Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems. CoRR abs/2307.12975 (2023) - [i101]Alexander Bukharin, Yixiao Li, Pengcheng He, Weizhu Chen, Tuo Zhao:
Deep Reinforcement Learning from Hierarchical Weak Preference Feedback. CoRR abs/2309.02632 (2023) - [i100]Zhenghao Xu, Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao:
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds. CoRR abs/2309.13915 (2023) - [i99]Chen Liang, Jiahui Yu, Ming-Hsuan Yang, Matthew Brown, Yin Cui, Tuo Zhao, Boqing Gong, Tianyi Zhou:
Module-wise Adaptive Distillation for Multimodality Foundation Models. CoRR abs/2310.04550 (2023) - [i98]Yixiao Li, Yifan Yu, Chen Liang, Pengcheng He, Nikos Karampatziakis, Weizhu Chen, Tuo Zhao:
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models. CoRR abs/2310.08659 (2023) - [i97]Alexander Bukharin, Yan Li, Yue Yu, Qingru Zhang, Zhehui Chen, Simiao Zuo, Chao Zhang, Songan Zhang, Tuo Zhao:
Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms. CoRR abs/2310.10810 (2023) - [i96]Qingru Zhang, Dhananjay Ram, Cole Hawkins, Sheng Zha, Tuo Zhao:
Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer. CoRR abs/2310.12442 (2023) - [i95]Zichong Li, Qunzhi Xu, Zhenghao Xu, Yajun Mei, Tuo Zhao, Hongyuan Zha:
Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process with Uncertainty Quantification. CoRR abs/2310.16310 (2023) - [i94]Zichong Li, Yanbo Xu, Simiao Zuo, Haoming Jiang, Chao Zhang, Tuo Zhao, Hongyuan Zha:
SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process. CoRR abs/2310.16336 (2023) - [i93]Yuqing Wang, Zhenghao Xu, Tuo Zhao, Molei Tao:
Good regularity creates large learning rate implicit biases: edge of stability, balancing, and catapult. CoRR abs/2310.17087 (2023) - [i92]Shenao Zhang, Boyi Liu, Zhaoran Wang, Tuo Zhao:
Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms. CoRR abs/2310.19927 (2023) - [i91]Qingru Zhang, Chandan Singh, Liyuan Liu, Xiaodong Liu, Bin Yu, Jianfeng Gao, Tuo Zhao:
Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs. CoRR abs/2311.02262 (2023) - [i90]Alexander Bukharin, Tuo Zhao:
Data Diversity Matters for Robust Instruction Tuning. CoRR abs/2311.14736 (2023) - 2022
- [j20]Zhen Shao, Lin Zhang, Susan A. Brown, Tuo Zhao:
Understanding users' trust transfer mechanism in a blockchain-enabled platform: A mixed methods study. Decis. Support Syst. 155: 113716 (2022) - [j19]Suliang Bu, Yunxin Zhao, Tuo Zhao, Shaojun Wang, Mei Han:
Modeling Speech Structure to Improve T-F Masks for Speech Enhancement and Recognition. IEEE ACM Trans. Audio Speech Lang. Process. 30: 2705-2715 (2022) - [c84]Chen Liang, Pengcheng He, Yelong Shen, Weizhu Chen, Tuo Zhao:
CAMERO: Consistency Regularized Ensemble of Perturbed Language Models with Weight Sharing. ACL (1) 2022: 7162-7175 - [c83]Tianyi Liu, Yan Li, Enlu Zhou, Tuo Zhao:
Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably. AISTATS 2022: 2784-2802 - [c82]Jiachen Yang, Ethan Wang, Rakshit Trivedi, Tuo Zhao, Hongyuan Zha:
Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learning. AAMAS 2022: 1436-1445 - [c81]Yan Li, Dhruv Choudhary, Xiaohan Wei, Baichuan Yuan, Bhargav Bhushanam, Tuo Zhao, Guanghui Lan:
Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits. ICLR 2022 - [c80]Chen Liang, Haoming Jiang, Simiao Zuo, Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, Tuo Zhao:
No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Models. ICLR 2022 - [c79]Yuqing Wang, Minshuo Chen, Tuo Zhao, Molei Tao:
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect. ICLR 2022 - [c78]Simiao Zuo, Xiaodong Liu, Jian Jiao, Young Jin Kim, Hany Hassan, Ruofei Zhang, Jianfeng Gao, Tuo Zhao:
Taming Sparsely Activated Transformer with Stochastic Experts. ICLR 2022 - [c77]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 - [c76]Qingru Zhang, Simiao Zuo, Chen Liang, Alexander Bukharin, Pengcheng He, Weizhu Chen, Tuo Zhao:
PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance. ICML 2022: 26809-26823 - [c75]Suliang Bu, Yunxin Zhao, Tuo Zhao:
Steering vector correction in MVDR beamformer for speech enhancement. INTERSPEECH 2022: 5468-5472 - [c74]Zhigen Zhao, Simiao Zuo, Tuo Zhao, Ye Zhao:
Adversarially Regularized Policy Learning Guided by Trajectory Optimization. L4DC 2022: 844-857 - [c73]Rui Feng, Chen Luo, Qingyu Yin, Bing Yin, Tuo Zhao, Chao Zhang:
CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data. NAACL-HLT 2022: 219-230 - [c72]Simiao Zuo, Yue Yu, Chen Liang, Haoming Jiang, Siawpeng Er, Chao Zhang, Tuo Zhao, Hongyuan Zha:
Self-Training with Differentiable Teacher. NAACL-HLT (Findings) 2022: 933-949 - [c71]Simiao Zuo, Qingru Zhang, Chen Liang, Pengcheng He, Tuo Zhao, Weizhu Chen:
MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation. NAACL-HLT 2022: 1610-1623 - [c70]Biraj Dahal, Alexander Havrilla, Minshuo Chen, Tuo Zhao, Wenjing Liao:
On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds. NeurIPS 2022 - [c69]Suliang Bu, Tuo Zhao, Yunxin Zhao:
TDOA Estimation of Speech Source in Noisy Reverberant Environments. SLT 2022: 1059-1066 - [i89]Hao Liu, Haizhao Yang, Minshuo Chen, Tuo Zhao, Wenjing Liao:
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces. CoRR abs/2201.00217 (2022) - [i88]Siawpeng Er, Edward Liu, Minshuo Chen, Yan Li, Yuqi Liu, Tuo Zhao, Hua Wang:
Deep Learning Assisted End-to-End Synthesis of mm-Wave Passive Networks with 3D EM Structures: A Study on A Transformer-Based Matching Network. CoRR abs/2201.02141 (2022) - [i87]Guanghui Lan, Yan Li, Tuo Zhao:
Block Policy Mirror Descent. CoRR abs/2201.05756 (2022) - [i86]Yan Li, Tuo Zhao, Guanghui Lan:
Homotopic Policy Mirror Descent: Policy Convergence, Implicit Regularization, and Improved Sample Complexity. CoRR abs/2201.09457 (2022) - [i85]Chen Liang, Haoming Jiang, Simiao Zuo, Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, Tuo Zhao:
No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Models. CoRR abs/2202.02664 (2022) - [i84]Tianyi Liu, Yan Li, Enlu Zhou, Tuo Zhao:
Noise Regularizes Over-parameterized Rank One Matrix Recovery, Provably. CoRR abs/2202.03535 (2022) - [i83]Rui Feng, Chen Luo, Qingyu Yin, Bing Yin, Tuo Zhao, Chao Zhang:
CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data. CoRR abs/2204.04303 (2022) - [i82]Chen Liang, Pengcheng He, Yelong Shen, Weizhu Chen, Tuo Zhao:
CAMERO: Consistency Regularized Ensemble of Perturbed Language Models with Weight Sharing. CoRR abs/2204.06625 (2022) - [i81]Simiao Zuo, Qingru Zhang, Chen Liang, Pengcheng He, Tuo Zhao, Weizhu Chen:
MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation. CoRR abs/2204.07675 (2022) - [i80]Jie Wang, Minshuo Chen, Tuo Zhao, Wenjing Liao, Yao Xie:
A Manifold Two-Sample Test Study: Integral Probability Metric with Neural Networks. CoRR abs/2205.02043 (2022) - [i79]Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao:
Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks. CoRR abs/2206.02887 (2022) - [i78]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) - [i77]Qingru Zhang, Simiao Zuo, Chen Liang, Alexander Bukharin, Pengcheng He, Weizhu Chen, Tuo Zhao:
PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance. CoRR abs/2206.12562 (2022) - [i76]Simiao Zuo, Tianyi Liu, Tuo Zhao, Hongyuan Zha:
Differentially Private Estimation of Hawkes Process. CoRR abs/2209.07303 (2022) - [i75]Simiao Zuo, Haoming Jiang, Qingyu Yin, Xianfeng Tang, Bing Yin, Tuo Zhao:
DiP-GNN: Discriminative Pre-Training of Graph Neural Networks. CoRR abs/2209.07499 (2022) - [i74]Simiao Zuo, Qingyu Yin, Haoming Jiang, Shaohui Xi, Bing Yin, Chao Zhang, Tuo Zhao:
Context-Aware Query Rewriting for Improving Users' Search Experience on E-commerce Websites. CoRR abs/2209.07584 (2022) - [i73]Yan Li, Tuo Zhao, Guanghui Lan:
First-order Policy Optimization for Robust Markov Decision Process. CoRR abs/2209.10579 (2022) - [i72]Chen Liang, Simiao Zuo, Qingru Zhang, Pengcheng He, Weizhu Chen, Tuo Zhao:
Less is More: Task-aware Layer-wise Distillation for Language Model Compression. CoRR abs/2210.01351 (2022) - [i71]Jiahui Cheng, Minshuo Chen, Hao Liu, Tuo Zhao, Wenjing Liao:
High Dimensional Binary Classification under Label Shift: Phase Transition and Regularization. CoRR abs/2212.00700 (2022) - [i70]Simiao Zuo, Xiaodong Liu, Jian Jiao, Denis Charles, Eren Manavoglu, Tuo Zhao, Jianfeng Gao:
Efficient Long Sequence Modeling via State Space Augmented Transformer. CoRR abs/2212.08136 (2022) - 2021
- [j18]Lewis Liu, Songtao Lu, Tuo Zhao, Zhaoran Wang:
Spectrum Truncation Power Iteration for Agnostic Matrix Phase Retrieval. IEEE Trans. Signal Process. 69: 3991-4006 (2021) - [c68]Haoming Jiang, Danqing Zhang, Tianyu Cao, Bing Yin, Tuo Zhao:
Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data. ACL/IJCNLP (1) 2021: 1775-1789 - [c67]Chen Liang, Simiao Zuo, Minshuo Chen, Haoming Jiang, Xiaodong Liu, Pengcheng He, Tuo Zhao, Weizhu Chen:
Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization. ACL/IJCNLP (1) 2021: 6524-6538 - [c66]Haoming Jiang, Zhehui Chen, Yuyang Shi, Bo Dai, Tuo Zhao:
Learning to Defend by Learning to Attack. AISTATS 2021: 577-585 - [c65]Tianyi Liu, Yan Li, Song Wei, Enlu Zhou, Tuo Zhao:
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization. AISTATS 2021: 1891-1899 - [c64]Danqing Zhang, Zheng Li, Tianyu Cao, Chen Luo, Tony Wu, Hanqing Lu, Yiwei Song, Bing Yin, Tuo Zhao, Qiang Yang:
QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction. CIKM 2021: 4362-4372 - [c63]Chen Liang, Haoming Jiang, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao, Tuo Zhao:
Token-wise Curriculum Learning for Neural Machine Translation. EMNLP (Findings) 2021: 3658-3670 - [c62]Simiao Zuo, Chen Liang, Haoming Jiang, Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, Tuo Zhao:
ARCH: Efficient Adversarial Regularized Training with Caching. EMNLP (Findings) 2021: 4118-4131 - [c61]Simiao Zuo, Chen Liang, Haoming Jiang, Xiaodong Liu, Pengcheng He, Jianfeng Gao, Weizhu Chen, Tuo Zhao:
Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach. EMNLP (1) 2021: 6562-6577 - [c60]Haoming Jiang, Bo Dai, Mengjiao Yang, Tuo Zhao, Wei Wei:
Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach. EMNLP (1) 2021: 7419-7451 - [c59]Yujia Xie, Yixiu Mao, Simiao Zuo, Hongteng Xu, Xiaojing Ye, Tuo Zhao, Hongyuan Zha:
A Hypergradient Approach to Robust Regression without Correspondence. ICLR 2021 - [c58]Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham M. Kakade, Huan Wang, Caiming Xiong:
How Important is the Train-Validation Split in Meta-Learning? ICML 2021: 543-553 - [c57]Hao Liu, Minshuo Chen, Tuo Zhao, Wenjing Liao:
Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks. ICML 2021: 6770-6780 - [c56]Tuo Zhao, Yunxin Zhao, Shaojun Wang, Mei Han:
UNet++-Based Multi-Channel Speech Dereverberation and Distant Speech Recognition. ISCSLP 2021: 1-5 - [c55]Yue Yu, Simiao Zuo, Haoming Jiang, Wendi Ren, Tuo Zhao, Chao Zhang:
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach. NAACL-HLT 2021: 1063-1077 - [c54]Minshuo Chen, Yan Li, Ethan Wang, Zhuoran Yang, Zhaoran Wang, Tuo Zhao:
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL. NeurIPS 2021: 17913-17926 - [i69]Haoming Jiang, Bo Dai, Mengjiao Yang, Wei Wei, Tuo Zhao:
Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach. CoRR abs/2102.10242 (2021) - [i68]Tianyi Liu, Yan Li, Song Wei, Enlu Zhou, Tuo Zhao:
Noisy Gradient Descent Converges to Flat Minima for Nonconvex Matrix Factorization. CoRR abs/2102.12430 (2021) - [i67]Jiachen Yang, Tarik Dzanic, Brenden K. Petersen, Jun Kudo, Ketan Mittal, Vladimir Z. Tomov, Jean-Sylvain Camier, Tuo Zhao, Hongyuan Zha, Tzanio V. Kolev, Robert W. Anderson, Daniel M. Faissol:
Reinforcement Learning for Adaptive Mesh Refinement. CoRR abs/2103.01342 (2021) - [i66]Chen Liang, Haoming Jiang, Xiaodong Liu, Pengcheng He, Weizhu Chen, Jianfeng Gao, Tuo Zhao:
Token-wise Curriculum Learning for Neural Machine Translation. CoRR abs/2103.11088 (2021) - [i65]Simiao Zuo, Chen Liang, Haoming Jiang, Xiaodong Liu, Pengcheng He, Jianfeng Gao, Weizhu Chen, Tuo Zhao:
Adversarial Training as Stackelberg Game: An Unrolled Optimization Approach. CoRR abs/2104.04886 (2021) - [i64]Siawpeng Er, Shihao Yang, Tuo Zhao:
COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction. CoRR abs/2105.00620 (2021) - [i63]Yan Li, Lingxiao Wang, Jiachen Yang, Ethan Wang, Zhaoran Wang, Tuo Zhao, Hongyuan Zha:
Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach. CoRR abs/2105.08268 (2021) - [i62]Chen Liang, Simiao Zuo, Minshuo Chen, Haoming Jiang, Xiaodong Liu, Pengcheng He, Tuo Zhao, Weizhu Chen:
Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization. CoRR abs/2105.12002 (2021) - [i61]Haoming Jiang, Danqing Zhang, Tianyu Cao, Bing Yin, Tuo Zhao:
Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data. CoRR abs/2106.08977 (2021) - [i60]Yan Li, Caleb Ju, Ethan X. Fang, Tuo Zhao:
Implicit Regularization of Bregman Proximal Point Algorithm and Mirror Descent on Separable Data. CoRR abs/2108.06808 (2021) - [i59]Danqing Zhang, Zheng Li, Tianyu Cao, Chen Luo, Tony Wu, Hanqing Lu, Yiwei Song, Bing Yin, Tuo Zhao, Qiang Yang:
QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction. CoRR abs/2108.08468 (2021) - [i58]Hao Liu, Minshuo Chen, Tuo Zhao, Wenjing Liao:
Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks. CoRR abs/2109.02832 (2021) - [i57]Simiao Zuo, Chen Liang, Haoming Jiang, Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, Tuo Zhao:
ARCH: Efficient Adversarial Regularized Training with Caching. CoRR abs/2109.07048 (2021) - [i56]Simiao Zuo, Yue Yu, Chen Liang, Haoming Jiang, Siawpeng Er, Chao Zhang, Tuo Zhao, Hongyuan Zha:
Self-Training with Differentiable Teacher. CoRR abs/2109.07049 (2021) - [i55]Zhigen Zhao, Simiao Zuo, Tuo Zhao, Ye Zhao:
Adversarially Regularized Policy Learning Guided by Trajectory Optimization. CoRR abs/2109.07627 (2021) - [i54]Yuqing Wang, Minshuo Chen, Tuo Zhao, Molei Tao:
Large Learning Rate Tames Homogeneity: Convergence and Balancing Effect. CoRR abs/2110.03677 (2021) - [i53]Simiao Zuo, Xiaodong Liu, Jian Jiao, Young Jin Kim, Hany Hassan, Ruofei Zhang, Tuo Zhao, Jianfeng Gao:
Taming Sparsely Activated Transformer with Stochastic Experts. CoRR abs/2110.04260 (2021) - [i52]Yan Li, Dhruv Choudhary, Xiaohan Wei, Baichuan Yuan, Bhargav Bhushanam, Tuo Zhao, Guanghui Lan:
Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits. CoRR abs/2110.04844 (2021) - [i51]Yunhai Han, Rahul Batra, Nathan Boyd, Tuo Zhao, Yu She, Seth Hutchinson, Ye Zhao:
Learning Generalizable Vision-Tactile Robotic Grasping Strategy for Deformable Objects via Transformer. CoRR abs/2112.06374 (2021) - [i50]Jiachen Yang, Ethan Wang, Rakshit Trivedi, Tuo Zhao, Hongyuan Zha:
Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learning. CoRR abs/2112.10859 (2021) - 2020
- [j17]Yifan Zhang, Jin Tian, Tuo Zhao, Shaohui Mei:
Spatial Resolution Enhancement of Remote Sensing Hyperspectral Images With Localized Spatial-Spectral Dictionary Pair. IEEE Access 8: 61051-61069 (2020) - [c53]Haoming Jiang, Chen Liang, Chong Wang, Tuo Zhao:
Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing. ACL 2020: 1823-1834 - [c52]Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, Tuo Zhao:
SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization. ACL 2020: 2177-2190 - [c51]Minshuo Chen, Xingguo Li, Tuo Zhao:
On Generalization Bounds of a Family of Recurrent Neural Networks. AISTATS 2020: 1233-1243 - [c50]Lingkai Kong, Haoming Jiang, Yuchen Zhuang, Jie Lyu, Tuo Zhao, Chao Zhang:
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data. EMNLP (1) 2020: 1326-1340 - [c49]Minshuo Chen, Yizhou Wang, Tianyi Liu, Zhuoran Yang, Xingguo Li, Zhaoran Wang, Tuo Zhao:
On Computation and Generalization of Generative Adversarial Imitation Learning. ICLR 2020 - [c48]Yan Li, Ethan X. Fang, Huan Xu, Tuo Zhao:
Implicit Bias of Gradient Descent based Adversarial Training on Separable Data. ICLR 2020 - [c47]Qianli Shen, Yan Li, Haoming Jiang, Zhaoran Wang, Tuo Zhao:
Deep Reinforcement Learning with Robust and Smooth Policy. ICML 2020: 8707-8718 - [c46]Simiao Zuo, Haoming Jiang, Zichong Li, Tuo Zhao, Hongyuan Zha:
Transformer Hawkes Process. ICML 2020: 11692-11702 - [c45]Chen Liang, Yue Yu, Haoming Jiang, Siawpeng Er, Ruijia Wang, Tuo Zhao, Chao Zhang:
BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision. KDD 2020: 1054-1064 - [c44]Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher:
Towards Understanding Hierarchical Learning: Benefits of Neural Representations. NeurIPS 2020 - [c43]Kaixuan Huang, Yuqing Wang, Molei Tao, Tuo Zhao:
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? - A Neural Tangent Kernel Perspective. NeurIPS 2020 - [c42]Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister:
Differentiable Top-k with Optimal Transport. NeurIPS 2020 - [c41]Lin Zhang, Zhen Shao, Tuo Zhao, Susan A. Brown:
The Role of Mobile Social Application in Stimulating Learning Stickiness in Blended Learning. PACIS 2020: 235 - [i49]Minshuo Chen, Yizhou Wang, Tianyi Liu, Zhuoran Yang, Xingguo Li, Zhaoran Wang, Tuo Zhao:
On Computation and Generalization of Generative Adversarial Imitation Learning. CoRR abs/2001.02792 (2020) - [i48]Minshuo Chen, Wenjing Liao, Hongyuan Zha, Tuo Zhao:
Statistical Guarantees of Generative Adversarial Networks for Distribution Estimation. CoRR abs/2002.03938 (2020) - [i47]Kaixuan Huang, Yuqing Wang, Molei Tao, Tuo Zhao:
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? - A Neural Tangent Kernel Perspective. CoRR abs/2002.06262 (2020) - [i46]Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister:
Differentiable Top-k Operator with Optimal Transport. CoRR abs/2002.06504 (2020) - [i45]Simiao Zuo, Haoming Jiang, Zichong Li, Tuo Zhao, Hongyuan Zha:
Transformer Hawkes Process. CoRR abs/2002.09291 (2020) - [i44]Qianli Shen, Yan Li, Haoming Jiang, Zhaoran Wang, Tuo Zhao:
Deep Reinforcement Learning with Smooth Policy. CoRR abs/2003.09534 (2020) - [i43]Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher:
Towards Understanding Hierarchical Learning: Benefits of Neural Representations. CoRR abs/2006.13436 (2020) - [i42]Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry A. Wasserman:
The huge Package for High-dimensional Undirected Graph Estimation in R. CoRR abs/2006.14781 (2020) - [i41]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) - [i40]Xingguo Li, Tuo Zhao, Xiaoming Yuan, Han Liu:
The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R. CoRR abs/2006.15419 (2020) - [i39]Chen Liang, Yue Yu, Haoming Jiang, Siawpeng Er, Ruijia Wang, Tuo Zhao, Chao Zhang:
BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision. CoRR abs/2006.15509 (2020) - [i38]David Joseph Munzer, Siawpeng Er, Minshuo Chen, Yan Li, Naga Sasikanth Mannem, Tuo Zhao, Hua Wang:
Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers. CoRR abs/2008.10755 (2020) - [i37]Yu Bai, Minshuo Chen, Pan Zhou, Tuo Zhao, Jason D. Lee, Sham M. Kakade, Huan Wang, Caiming Xiong:
How Important is the Train-Validation Split in Meta-Learning? CoRR abs/2010.05843 (2020) - [i36]Yue Yu, Simiao Zuo, Haoming Jiang, Wendi Ren, Tuo Zhao, Chao Zhang:
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach. CoRR abs/2010.07835 (2020) - [i35]Lingkai Kong, Haoming Jiang, Yuchen Zhuang, Jie Lyu, Tuo Zhao, Chao Zhang:
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data. CoRR abs/2010.11506 (2020) - [i34]Minshuo Chen, Hao Liu, Wenjing Liao, Tuo Zhao:
Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks. CoRR abs/2011.01797 (2020) - [i33]Yujia Xie, Yixiu Mao, Simiao Zuo, Hongteng Xu, Xiaojing Ye, Tuo Zhao, Hongyuan Zha:
A Hypergradient Approach to Robust Regression without Correspondence. CoRR abs/2012.00123 (2020)
2010 – 2019
- 2019
- [j16]Gelan Yang, Wei Tan, Huixia Jin, Tuo Zhao, Li Tu:
Review wearable sensing system for gait recognition. Clust. Comput. 22(Supplement): 3021-3029 (2019) - [j15]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) - [j14]Zhuoran Yang, Lin F. Yang, Ethan X. Fang, Tuo Zhao, Zhaoran Wang, Matey Neykov:
Misspecified nonconvex statistical optimization for sparse phase retrieval. Math. Program. 176(1-2): 545-571 (2019) - [j13]Xingguo Li, Junwei Lu, Raman Arora, Jarvis D. Haupt, Han Liu, Zhaoran Wang, Tuo Zhao:
Symmetry, Saddle Points, and Global Optimization Landscape of Nonconvex Matrix Factorization. IEEE Trans. Inf. Theory 65(6): 3489-3514 (2019) - [c40]Zhehui Chen, Xingguo Li, Lin Yang, Jarvis D. Haupt, Tuo Zhao:
On Constrained Nonconvex Stochastic Optimization: A Case Study for Generalized Eigenvalue Decomposition. AISTATS 2019: 916-925 - [c39]Zhehui Chen, Haoming Jiang, Yuyang Shi, Bo Dai, Tuo Zhao:
Learning to Defense by Learning to Attack. DGS@ICLR 2019 - [c38]Haoming Jiang, Zhehui Chen, Minshuo Chen, Feng Liu, Dingding Wang, Tuo Zhao:
On Computation and Generalization of Generative Adversarial Networks under Spectrum Control. ICLR (Poster) 2019 - [c37]Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha:
On Scalable and Efficient Computation of Large Scale Optimal Transport. DGS@ICLR 2019 - [c36]Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha:
On Scalable and Efficient Computation of Large Scale Optimal Transport. ICML 2019: 6882-6892 - [c35]Mo Zhou, Tianyi Liu, Yan Li, Dachao Lin, Enlu Zhou, Tuo Zhao:
Toward Understanding the Importance of Noise in Training Neural Networks. ICML 2019: 7594-7602 - [c34]Tianyi Liu, Minshuo Chen, Mo Zhou, Simon S. Du, Enlu Zhou, Tuo Zhao:
Towards Understanding the Importance of Shortcut Connections in Residual Networks. NeurIPS 2019: 7890-7900 - [c33]Minshuo Chen, Haoming Jiang, Wenjing Liao, Tuo Zhao:
Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds. NeurIPS 2019: 8172-8182 - [c32]Yujia Xie, Haoming Jiang, Feng Liu, Tuo Zhao, Hongyuan Zha:
Meta Learning with Relational Information for Short Sequences. NeurIPS 2019: 9901-9912 - [c31]Xingguo Li, Haoming Jiang, Jarvis D. Haupt, Raman Arora, Han Liu, Mingyi Hong, Tuo Zhao:
On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don't Worry About its Nonsmooth Loss Function. UAI 2019: 49-59 - [c30]Lin F. Yang, Zheng Yu, Vladimir Braverman, Tuo Zhao, Mengdi Wang:
Online Factorization and Partition of Complex Networks by Random Walk. UAI 2019: 820-830 - [i32]Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha:
On Scalable and Efficient Computation of Large Scale Optimal Transport. CoRR abs/1905.00158 (2019) - [i31]Yan Li, Ethan X. Fang, Huan Xu, Tuo Zhao:
Inductive Bias of Gradient Descent based Adversarial Training on Separable Data. CoRR abs/1906.02931 (2019) - [i30]Minshuo Chen, Haoming Jiang, Wenjing Liao, Tuo Zhao:
Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds. CoRR abs/1908.01842 (2019) - [i29]Yujia Xie, Haoming Jiang, Feng Liu, Tuo Zhao, Hongyuan Zha:
Meta Learning with Relational Information for Short Sequences. CoRR abs/1909.02105 (2019) - [i28]Mo Zhou, Tianyi Liu, Yan Li, Dachao Lin, Enlu Zhou, Tuo Zhao:
Towards Understanding the Importance of Noise in Training Neural Networks. CoRR abs/1909.03172 (2019) - [i27]Tianyi Liu, Minshuo Chen, Mo Zhou, Simon S. Du, Enlu Zhou, Tuo Zhao:
Towards Understanding the Importance of Shortcut Connections in Residual Networks. CoRR abs/1909.04653 (2019) - [i26]Minshuo Chen, Xingguo Li, Tuo Zhao:
On Generalization Bounds of a Family of Recurrent Neural Networks. CoRR abs/1910.12947 (2019) - [i25]Haoming Jiang, Chen Liang, Chong Wang, Tuo Zhao:
Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain Mixing. CoRR abs/1911.02692 (2019) - [i24]Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, Tuo Zhao:
SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization. CoRR abs/1911.03437 (2019) - 2018
- [c29]Xingguo Li, Jarvis D. Haupt, Junwei Lu, Zhaoran Wang, Raman Arora, Han Liu, Tuo Zhao:
Symmetry. Saddle Points, and Global Optimization Landscape of Nonconvex Matrix Factorization. ITA 2018: 1-9 - [c28]Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao:
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization. NeurIPS 2018: 3500-3510 - [c27]Tianyi Liu, Shiyang Li, Jianping Shi, Enlu Zhou, Tuo Zhao:
Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization. NeurIPS 2018: 3686-3696 - [c26]Lin F. Yang, Raman Arora, Vladimir Braverman, Tuo Zhao:
The Physical Systems Behind Optimization Algorithms. NeurIPS 2018: 4377-4386 - [c25]Ming Yu, Zhuoran Yang, Tuo Zhao, Mladen Kolar, Zhaoran Wang:
Provable Gaussian Embedding with One Observation. NeurIPS 2018: 6765-6775 - [i23]Tianyi Liu, Zhehui Chen, Enlu Zhou, Tuo Zhao:
Toward Deeper Understanding of Nonconvex Stochastic Optimization with Momentum using Diffusion Approximations. CoRR abs/1802.05155 (2018) - [i22]Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao:
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization. CoRR abs/1803.02312 (2018) - [i21]Yingxiang Yang, Adams Wei Yu, Zhaoran Wang, Tuo Zhao:
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models. CoRR abs/1803.03919 (2018) - [i20]Tianyi Liu, Shiyang Li, Jianping Shi, Enlu Zhou, Tuo Zhao:
Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization. CoRR abs/1806.01660 (2018) - [i19]Zhehui Chen, Xingguo Li, Lin F. Yang, Jarvis D. Haupt, Tuo Zhao:
On Landscape of Lagrangian Functions and Stochastic Search for Constrained Nonconvex Optimization. CoRR abs/1806.05151 (2018) - [i18]Xingguo Li, Junwei Lu, Zhaoran Wang, Jarvis D. Haupt, Tuo Zhao:
On Tighter Generalization Bound for Deep Neural Networks: CNNs, ResNets, and Beyond. CoRR abs/1806.05159 (2018) - [i17]Ming Yu, Zhuoran Yang, Tuo Zhao, Mladen Kolar, Zhaoran Wang:
Provable Gaussian Embedding with One Observation. CoRR abs/1810.11098 (2018) - [i16]Zhehui Chen, Haoming Jiang, Bo Dai, Tuo Zhao:
Learning to Defense by Learning to Attack. CoRR abs/1811.01213 (2018) - [i15]Haoming Jiang, Zhehui Chen, Minshuo Chen, Feng Liu, Dingding Wang, Tuo Zhao:
On Computation and Generalization of GANs with Spectrum Control. CoRR abs/1812.10912 (2018) - 2017
- [j12]Jiuwen Cao, Tuo Zhao, Jianzhong Wang, Ruirong Wang, Yun Chen:
Excavation equipment classification based on improved MFCC features and ELM. Neurocomputing 261: 231-241 (2017) - [j11]Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Mingyi Hong:
On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization. J. Mach. Learn. Res. 18: 184:1-184:24 (2017) - [j10]Jiuwen Cao, Wuhao Huang, Tuo Zhao, Jianzhong Wang, Ruirong Wang:
An enhance excavation equipments classification algorithm based on acoustic spectrum dynamic feature. Multidimens. Syst. Signal Process. 28(3): 921-943 (2017) - [c24]Yifan Zhang, Tuo Zhao, Mingyi He:
Hyperspectral and multispectral image fusion using local spatial-spectral dictionary pair. APSIPA 2017: 242-246 - [c23]Zhehui Chen, Lin F. Yang, Chris Junchi Li, Tuo Zhao:
Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability. ICML 2017: 777-786 - [c22]Gang Liu, Qi Qian, Zhibin Wang, Qingen Zhao, Tianzhou Wang, Hao Li, Jian Xue, Shenghuo Zhu, Rong Jin, Tuo Zhao:
The Opensesame NIST 2016 Speaker Recognition Evaluation System. INTERSPEECH 2017: 2854-2858 - [c21]Haotian Pang, Han Liu, Robert J. Vanderbei, Tuo Zhao:
Parametric Simplex Method for Sparse Learning. NIPS 2017: 188-197 - [c20]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 - [c19]Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhen Liu, Bo Dai, Tuo Zhao, Le Song:
Deep Hyperspherical Learning. NIPS 2017: 3950-3960 - [i14]Zhehui Chen, Forest L. Yang, Chris Junchi Li, Tuo Zhao:
Online Multiview Representation Learning: Dropping Convexity for Better Efficiency. CoRR abs/1702.08134 (2017) - [i13]Haotian Pang, Tuo Zhao, Robert J. Vanderbei, Han Liu:
Homotopy Parametric Simplex Method for Sparse Learning. CoRR abs/1704.01079 (2017) - [i12]Lin F. Yang, Vladimir Braverman, Tuo Zhao, Mengdi Wang:
Dynamic Factorization and Partition of Complex Networks. CoRR abs/1705.07881 (2017) - [i11]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) - [i10]Weiyang Liu, Yan-Ming Zhang, Xingguo Li, Zhiding Yu, Bo Dai, Tuo Zhao, Le Song:
Deep Hyperspherical Learning. CoRR abs/1711.03189 (2017) - [i9]Zhuoran Yang, Lin F. Yang, Ethan X. Fang, Tuo Zhao, Zhaoran Wang, Matey Neykov:
Misspecified Nonconvex Statistical Optimization for Phase Retrieval. CoRR abs/1712.06245 (2017) - 2016
- [j9]Tuo Zhao, Yunxin Zhao, Xin Chen:
Ensemble Acoustic Modeling for CD-DNN-HMM Using Random Forests of Phonetic Decision Trees. J. Signal Process. Syst. 82(2): 187-196 (2016) - [c18]Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Mingyi Hong:
An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization. AISTATS 2016: 491-499 - [c17]Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Jarvis D. Haupt:
Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning. ICML 2016: 917-925 - [c16]Xiaoqin Xue, Yifan Zhang, Tuo Zhao, Mingyi He:
Subpixel mapping of hyperspectral images based on collaborative representation. IGARSS 2016: 3298-3301 - [c15]Tuo Zhao, Yifan Zhang, Xiaoqin Xue, Mingyi He:
Hyperspectral and multispectral image fusion using collaborative representation with local adaptive dictionary pair. IGARSS 2016: 7212-7215 - [c14]Davood Hajinezhad, Mingyi Hong, Tuo Zhao, Zhaoran Wang:
NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization. NIPS 2016: 3207-3215 - [i8]Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Jarvis D. Haupt:
Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning. CoRR abs/1605.02711 (2016) - [i7]Davood Hajinezhad, Mingyi Hong, Tuo Zhao, Zhaoran Wang:
NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization. CoRR abs/1605.07747 (2016) - [i6]Xingguo Li, Jarvis D. Haupt, Raman Arora, Han Liu, Mingyi Hong, Tuo Zhao:
A First Order Free Lunch for SQRT-Lasso. CoRR abs/1605.07950 (2016) - [i5]Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Mingyi Hong:
An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization. CoRR abs/1607.02793 (2016) - [i4]Lin F. Yang, Raman Arora, Vladimir Braverman, Tuo Zhao:
The Physical Systems Behind Optimization Algorithms. CoRR abs/1612.02803 (2016) - [i3]Xingguo Li, Zhaoran Wang, Junwei Lu, Raman Arora, Jarvis D. Haupt, Han Liu, Tuo Zhao:
Symmetry, Saddle Points, and Global Geometry of Nonconvex Matrix Factorization. CoRR abs/1612.09296 (2016) - 2015
- [j8]Xingguo Li, Tuo Zhao, Xiaoming Yuan, Han Liu:
The flare package for high dimensional linear regression and precision matrix estimation in R. J. Mach. Learn. Res. 16: 553-557 (2015) - [j7]Han Liu, Lie Wang, Tuo Zhao:
Calibrated multivariate regression with application to neural semantic basis discovery. J. Mach. Learn. Res. 16: 1579-1606 (2015) - [c13]Tuo Zhao, Yunxin Zhao, Xin Chen:
Time-frequency kernel-based CNN for speech recognition. INTERSPEECH 2015: 1888-1892 - [c12]Tuo Zhao, Zhaoran Wang, Han Liu:
A Nonconvex Optimization Framework for Low Rank Matrix Estimation. NIPS 2015: 559-567 - 2014
- [j6]Tuo Zhao, Han Liu:
Calibrated Precision Matrix Estimation for High-Dimensional Elliptical Distributions. IEEE Trans. Inf. Theory 60(12): 7874-7887 (2014) - [c11]Yunxin Zhao, Tuo Zhao, Xin Chen:
Multilevel sampling and aggregation for discriminative training. ISCSLP 2014: 93-97 - [c10]Tuo Zhao, Yunxin Zhao, Xin Chen:
Building an ensemble of CD-DNN-HMM acoustic model using random forests of phonetic decision trees. ISCSLP 2014: 98-102 - [c9]Han Liu, Lie Wang, Tuo Zhao:
Multivariate Regression with Calibration. NIPS 2014: 127-135 - [c8]Tuo Zhao, Mo Yu, Yiming Wang, Raman Arora, Han Liu:
Accelerated Mini-batch Randomized Block Coordinate Descent Method. NIPS 2014: 3329-3337 - [i2]Tuo Zhao, Han Liu, Tong Zhang:
Pathwise Coordinate Optimization for Sparse Learning: Algorithm and Theory. CoRR abs/1412.7477 (2014) - 2013
- [j5]Fang Han, Tuo Zhao, Han Liu:
CODA: high dimensional copula discriminant analysis. J. Mach. Learn. Res. 14(1): 629-671 (2013) - [c7]Tuo Zhao, Han Liu:
Sparse Inverse Covariance Estimation with Calibration. NIPS 2013: 2274-2282 - [i1]Han Liu, Lie Wang, Tuo Zhao:
Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery. CoRR abs/1305.2238 (2013) - 2012
- [j4]Tuo Zhao, Han Liu, Kathryn Roeder, John D. Lafferty, Larry A. Wasserman:
The huge Package for High-dimensional Undirected Graph Estimation in R. J. Mach. Learn. Res. 13: 1059-1062 (2012) - [c6]Tuo Zhao, Kathryn Roeder, Han Liu:
Smooth-projected Neighborhood Pursuit for High-dimensional Nonparanormal Graph Estimation. NIPS 2012: 162-170 - [c5]Tuo Zhao, Han Liu:
Sparse Additive Machine. AISTATS 2012: 1435-1443 - 2010
- [j3]Zhizheng Liang, Youfu Li, Tuo Zhao:
Projected gradient method for kernel discriminant nonnegative matrix factorization and the applications. Signal Process. 90(7): 2150-2163 (2010)
2000 – 2009
- 2009
- [j2]Quan Zou, Tuo Zhao, Yang Liu, Maozu Guo:
Predicting RNA secondary structure based on the class information and Hopfield network. Comput. Biol. Medicine 39(3): 206-214 (2009) - 2008
- [j1]Tuo Zhao, Zhizheng Liang, David Zhang, Quan Zou:
Interest filter vs. interest operator: Face recognition using Fisher linear discriminant based on interest filter representation. Pattern Recognit. Lett. 29(13): 1849-1857 (2008) - 2007
- [c4]Tuo Zhao, Zhizheng Liang, David Zhang, Yahui Liu:
A Novel Null Space-Based Kernel Discriminant Analysis for Face Recognition. ICB 2007: 547-556 - 2006
- [c3]Xiaoyue Jiang, Tuo Zhao, Rongchun Zhao:
Curve Mapping Based Illumination Adjustment for Face Detection. ACIVS 2006: 687-698 - [c2]Zhizheng Liang, Tuo Zhao:
Feature selection for linear support vector machines. ICPR (2) 2006: 606-609 - 2005
- [c1]Xiaoyue Jiang, Tuo Zhao, Rong Xiao, Rongchun Zhao:
Re-lighting and Compensation for Face Images. CAIP 2005: 288-295
Coauthor Index
[j26] [j25] [c97] [c95] [c93] [c89] [i110] [i108] [i107] [i104] [i103] [i102] [i100] [c79] [c77] [c70] [i89] [i88] [i80] [i79] [i78] [i71] [c67] [c58] [c57] [c54] [i62] [i58] [i54] [c51] [c49] [c44] [c42] [i49] [i48] [i46] [i43] [i38] [i37] [i34] [c38] [c37] [c36] [c34] [c33] [i32] [i30] [i27] [i26] [c28] [i22] [i15]
[c110] [c109] [i116] [i114] [i112] [c102] [c96] [c92] [c88] [i109] [i94] [c80] [c72] [i85] [i75] [i74] [c68] [c67] [c66] [c63] [c62] [c61] [c60] [c55] [i69] [i66] [i65] [i62] [i61] [i57] [i56] [c53] [c52] [c50] [c47] [c46] [c45] [i45] [i44] [i41] [i39] [i36] [i35] [j15] [c39] [c38] [c37] [c36] [c33] [c32] [c31] [i32] [i30] [i29] [i25] [i24] [i16] [i15]
Lin Yang 0011
aka: Lin F. Yang
aka: Lin F. Yang
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