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ORCIDHan Liu 0001
Person information
- affiliation: Northwestern University, Evanston, IL, USA
- affiliation: Princeton University, Department of Operations Research and Financial Engineering, NJ, USA
- affiliation: Johns Hopkins University, Department of Biostatistics and Computer Science, Baltimore, MD, USA
- affiliation (PhD 2011): Carnegie Mellon University, Pittsburgh, PA, USA
- affiliation (former): University of Toronto, Department of Computer Science, ON, Canada
Other persons with the same name
- Han Liu — disambiguation page
- Han Liu 0002
![[0000-0002-7731-8258]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0002-7731-8258")
— Shenzhen University, Guangdong, China (and 2 more) - Han Liu 0003 — King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
- Han Liu 0004 — Harbin Institute of Technology, School of Transportation Science and Engineering, China
- Han Liu 0005 — Sun Yat-Sen University, School of Geography and Planning, Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, Guangzhou, China
- Han Liu 0006 — Southeast University, School of Electrical Engineering, Nanjing, China (and 1 more)
- Han Liu 0007 — Xi'an University of Technology, Faculty of Automation and Information Engineering, Xi'an, China (and 1 more)
- Han Liu 0008 — Dalian University of Technology, School of Software, Dalian, China (and 1 more)
- Han Liu 0009 — Shanghai Maritime University, College of Information Engineering, Shanghai, China
- Han Liu 0010 — Oxford-Hainan Blockchain Research Institute, Haikou, China (and 2 more)
- Han Liu 0011 — Advanced Micro Devices (AMD) Inc., Beijing, China
- Han Liu 0012 — Hong Kong University of Science and Technology, Hong Kong (and 1 more)
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2020 – today
- 2024
[c100]Zhihan Zhou, Yanrong Ji, Weijian Li, Pratik Dutta, Ramana V. Davuluri, Han Liu:
DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genomes. ICLR 2024- [c99]Dennis Wu, Jerry Yao-Chieh Hu, Weijian Li, Bo-Yu Chen, Han Liu:
STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction. ICLR 2024 - [c98]Jerry Yao-Chieh Hu, Pei-Hsuan Chang, Haozheng Luo, Hong-Yu Chen, Weijian Li, Wei-Po Wang, Han Liu:
Outlier-Efficient Hopfield Layers for Large Transformer-Based Models. ICML 2024 - [c97]Jerry Yao-Chieh Hu, Thomas Lin, Zhao Song, Han Liu:
On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis. ICML 2024 - [c96]Dennis Wu, Jerry Yao-Chieh Hu, Teng-Yun Hsiao, Han Liu:
Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models. ICML 2024 - [c95]Chenwei Xu, Yu-Chao Huang, Jerry Yao-Chieh Hu, Weijian Li, Ammar Gilani, Hsi-Sheng Goan, Han Liu:
BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model. ICML 2024 - [c94]Guo Ye, Qinjie Lin, Zening Luo, Han Liu:
DOS®: A Deployment Operating System for Robots. ICRA 2024: 14086-14092 - [c93]Rui Shi, Seda Ogrenci, J. M. Arnold, J. R. Berlioz, Pierrick Hanlet, Kyle J. Hazelwood, M. A. Ibrahim, Han Liu, V. P. Nagaslaev, Aakaash Narayanan, D. J. Nicklaus, Jovan Mitrevski, Gauri Pradhan, A. L. Saewert, B. A. Schupbach, Kiyomi Seiya, Mattson Thieme, R. M. Thurman-Keup, N. V. Tran:
ML-Based Real-Time Control at the Edge: An Approach Using hls4ml. IPDPS (Workshops) 2024: 191 - [i70]Jerry Yao-Chieh Hu, Thomas Lin, Zhao Song, Han Liu:
On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis. CoRR abs/2402.04520 (2024) - [i69]Zhihan Zhou, Weimin Wu, Harrison Ho, Jiayi Wang, Lizhen Shi, Ramana V. Davuluri, Zhong Wang, Han Liu:
DNABERT-S: Learning Species-Aware DNA Embedding with Genome Foundation Models. CoRR abs/2402.08777 (2024) - [i68]Tim Tsz-Kit Lau, Han Liu, Mladen Kolar:
AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods. CoRR abs/2402.11215 (2024) - [i67]Zhihan Zhou, Qixiang Fang, Leonardo Neves, Francesco Barbieri, Yozen Liu, Han Liu, Maarten W. Bos, Ron Dotsch:
USE: Dynamic User Modeling with Stateful Sequence Models. CoRR abs/2403.13344 (2024) - [i66]Zhenyu Pan, Haozheng Luo, Manling Li, Han Liu:
Chain-of-Action: Faithful and Multimodal Question Answering through Large Language Models. CoRR abs/2403.17359 (2024) - [i65]Dennis Wu, Jerry Yao-Chieh Hu, Teng-Yun Hsiao, Han Liu:
Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models. CoRR abs/2404.03827 (2024) - [i64]Jerry Yao-Chieh Hu, Pei-Hsuan Chang, Robin Luo, Hong-Yu Chen, Weijian Li, Wei-Po Wang, Han Liu:
Outlier-Efficient Hopfield Layers for Large Transformer-Based Models. CoRR abs/2404.03828 (2024) - [i63]Chenwei Xu, Yu-Chao Huang, Jerry Yao-Chieh Hu, Weijian Li, Ammar Gilani, Hsi-Sheng Goan, Han Liu:
BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model. CoRR abs/2404.03830 (2024) - [i62]Jerry Yao-Chieh Hu, Bo-Yu Chen, Dennis Wu, Feng Ruan, Han Liu:
Nonparametric Modern Hopfield Models. CoRR abs/2404.03900 (2024) - [i61]Zhenyu Pan, Yoonsung Jeong, Xiaoda Liu, Han Liu:
HeteGraph-Mamba: Heterogeneous Graph Learning via Selective State Space Model. CoRR abs/2405.13915 (2024) - [i60]Zhenyu Pan, Haozheng Luo, Manling Li, Han Liu:
Conv-CoA: Improving Open-domain Question Answering in Large Language Models via Conversational Chain-of-Action. CoRR abs/2405.17822 (2024) - [i59]Jiahao Yu, Haozheng Luo, Jerry Yao-Chieh Hu, Wenbo Guo, Han Liu, Xinyu Xing:
Enhancing Jailbreak Attack Against Large Language Models through Silent Tokens. CoRR abs/2405.20653 (2024) - [i58]Haozheng Luo, Jiahao Yu, Wenxin Zhang, Jialong Li, Jerry Yao-Chieh Hu, Xinyu Xing, Han Liu:
Decoupled Alignment for Robust Plug-and-Play Adaptation. CoRR abs/2406.01514 (2024) - [i57]Jerry Yao-Chieh Hu, Maojiang Su, En-Jui Kuo, Zhao Song, Han Liu:
Computational Limits of Low-Rank Adaptation (LoRA) for Transformer-Based Models. CoRR abs/2406.03136 (2024) - [i56]Tim Tsz-Kit Lau, Weijian Li, Chenwei Xu, Han Liu, Mladen Kolar:
Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods. CoRR abs/2406.13936 (2024) - [i55]Jerry Yao-Chieh Hu, Weimin Wu, Zhuoru Li, Zhao Song, Han Liu:
On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs). CoRR abs/2407.01079 (2024) - [i54]Erzhi Liu, Jerry Yao-Chieh Hu, Alex Daniel Reneau, Zhao Song, Han Liu:
Differentially Private Kernel Density Estimation. CoRR abs/2409.01688 (2024) - [i53]Jaeyeon Jang, Diego Klabjan, Han Liu, Nital S. Patel, Xiuqi Li, Balakrishnan Ananthanarayanan, Husam Dauod, Tzung-Han Juang:
Scalable Multi-agent Reinforcement Learning for Factory-wide Dynamic Scheduling. CoRR abs/2409.13571 (2024) - [i52]Zhi Zhang, Chris Chow, Yasi Zhang, Yanchao Sun, Haochen Zhang, Eric Hanchen Jiang, Han Liu, Furong Huang, Yuchen Cui, Oscar Hernan Madrid Padilla:
Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayesian Theory. CoRR abs/2411.00401 (2024) - 2023
- [c92]Zhenyu Pan, Anshujit Sharma, Jerry Yao-Chieh Hu, Zhuo Liu, Ang Li, Han Liu, Michael C. Huang, Tong Geng:
Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under Uncertainty. AAAI 2023: 9354-9363 - [c91]Alex Daniel Reneau, Jerry Yao-Chieh Hu, Ammar Gilani, Han Liu:
Feature Programming for Multivariate Time Series Prediction. ICML 2023: 29009-29029 - [c90]Qinjie Lin, Guo Ye, Han Liu:
EMS®: A Massive Computational Experiment Management System towards Data-driven Robotics. ICRA 2023: 9068-9075 - [c89]Jerry Yao-Chieh Hu, Donglin Yang, Dennis Wu, Chenwei Xu, Bo-Yu Chen, Han Liu:
On Sparse Modern Hopfield Model. NeurIPS 2023 - [i51]Tim Tsz-Kit Lau, Han Liu, Thomas Pock:
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms. CoRR abs/2305.15988 (2023) - [i50]Alex Daniel Reneau, Jerry Yao-Chieh Hu, Chenwei Xu, Weijian Li, Ammar Gilani, Han Liu:
Feature Programming for Multivariate Time Series Prediction. CoRR abs/2306.06252 (2023) - [i49]Zhihan Zhou, Yanrong Ji, Weijian Li, Pratik Dutta, Ramana V. Davuluri, Han Liu:
DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome. CoRR abs/2306.15006 (2023) - [i48]Jaeyeon Jang, Diego Klabjan, Han Liu, Nital S. Patel, Xiuqi Li, Balakrishnan Ananthanarayanan, Husam Dauod, Tzung-Han Juang:
Learning Multiple Coordinated Agents under Directed Acyclic Graph Constraints. CoRR abs/2307.07529 (2023) - [i47]Guanlin Liu, Zhihan Zhou, Han Liu, Lifeng Lai:
Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty. CoRR abs/2307.07666 (2023) - [i46]Jerry Yao-Chieh Hu, Donglin Yang, Dennis Wu, Chenwei Xu, Bo-Yu Chen, Han Liu:
On Sparse Modern Hopfield Model. CoRR abs/2309.12673 (2023) - [i45]Rui Shi, Seda Ogrenci, J. M. Arnold, J. R. Berlioz, Pierrick Hanlet, Kyle J. Hazelwood, M. A. Ibrahim, Han Liu, V. P. Nagaslaev, Aakaash Narayanan, D. J. Nicklaus, Jovan Mitrevski, Gauri Pradhan, A. L. Saewert, B. A. Schupbach, Kiyomi Seiya, Mattson Thieme, R. M. Thurman-Keup, N. V. Tran:
ML-based Real-Time Control at the Edge: An Approach Using hls4ml. CoRR abs/2311.05716 (2023) - [i44]Quanquan Gu, Zhaoran Wang, Han Liu:
Sparse PCA with Oracle Property. CoRR abs/2312.16793 (2023) - [i43]Dennis Wu, Jerry Yao-Chieh Hu, Weijian Li, Bo-Yu Chen, Han Liu:
STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction. CoRR abs/2312.17346 (2023) - [i42]Chenwei Xu, Jerry Yao-Chieh Hu, Aakaash Narayanan, Mattson Thieme, Vladimir Nagaslaev, Mark Austin, Jeremy Arnold, Jose Berlioz, Pierrick Hanlet, Aisha Ibrahim, Dennis Nicklaus, Jovan Mitrevski, Jason Michael St. John, Gauri Pradhan, Andrea Saewert, Kiyomi Seiya, Brian Schupbach, Randy Thurman-Keup, Nhan Tran, Rui Shi, Seda Ogrenci, Alexis Maya-Isabelle Shuping, Kyle J. Hazelwood, Han Liu:
Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e. CoRR abs/2312.17372 (2023) - 2022
- [c88]Zhi Zhang, Zhuoran Yang, Han Liu, Pratap Tokekar, Furong Huang:
Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory. ICLR 2022 - [c87]Tim Tsz-Kit Lau, Han Liu:
Bregman Proximal Langevin Monte Carlo via Bregman-Moreau Envelopes. ICML 2022: 12049-12077 - [i41]Qinjie Lin, Han Liu, Biswa Sengupta:
Switch Trajectory Transformer with Distributional Value Approximation for Multi-Task Reinforcement Learning. CoRR abs/2203.07413 (2022) - [i40]Guo Ye, Han Liu, Biswa Sengupta:
Learning to Infer Belief Embedded Communication. CoRR abs/2203.07832 (2022) - [i39]Tim Tsz-Kit Lau, Han Liu:
Wasserstein Distributionally Robust Optimization via Wasserstein Barycenters. CoRR abs/2203.12136 (2022) - [i38]Ning Wang, Han Liu, Diego Klabjan:
Large-Scale Multi-Document Summarization with Information Extraction and Compression. CoRR abs/2205.00548 (2022) - [i37]Tim Tsz-Kit Lau, Han Liu:
Bregman Proximal Langevin Monte Carlo via Bregman-Moreau Envelopes. CoRR abs/2207.04387 (2022) - [i36]Chunyu Ma, Zhihan Zhou, Han Liu, David Koslicki:
Predicting Drug Repurposing Candidates and Their Mechanisms from A Biomedical Knowledge Graph. CoRR abs/2212.01384 (2022) - 2021
- [j38]Yanrong Ji, Zhihan Zhou, Han Liu, Ramana V. Davuluri:
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome. Bioinform. 37(15): 2112-2120 (2021) - [j37]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) - [j36]Junwei Lu, Fang Han, Han Liu:
Robust Scatter Matrix Estimation for High Dimensional Distributions With Heavy Tail. IEEE Trans. Inf. Theory 67(8): 5283-5304 (2021) - [c86]Zhihan Zhou, Liqian Ma, Han Liu:
Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading. ACL/IJCNLP (Findings) 2021: 2114-2124 - [c85]Qinjie Lin, Guo Ye, Jiayi Wang, Han Liu:
RoboFlow: a Data-centric Workflow Management System for Developing AI-enhanced Robots. CoRL 2021: 1789-1794 - [i35]Zhihan Zhou, Liqian Ma, Han Liu:
Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading. CoRR abs/2105.12825 (2021) - 2020
- [j35]Matey Neykov, Zhaoran Wang, Han Liu:
Agnostic Estimation for Phase Retrieval. J. Mach. Learn. Res. 21: 121:1-121:39 (2020) - [j34]Xiang Lyu, Will Wei Sun, Zhaoran Wang, Han Liu, Jian Yang, Guang Cheng:
Tensor Graphical Model: Non-Convex Optimization and Statistical Inference. IEEE Trans. Pattern Anal. Mach. Intell. 42(8): 2024-2037 (2020) - [c84]Yutai Hou, Wanxiang Che, Yongkui Lai, Zhihan Zhou, Yijia Liu, Han Liu, Ting Liu:
Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network. ACL 2020: 1381-1393 - [c83]Binghong Chen, Bo Dai, Qinjie Lin, Guo Ye, Han Liu, Le Song:
Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees. ICLR 2020 - [c82]Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinivas Aluru, Han Liu, Le Song:
GLAD: Learning Sparse Graph Recovery. ICLR 2020 - [c81]Guo Ye, Qinjie Lin, Tzung-Han Juang, Han Liu:
Collision-free Navigation of Human-centered Robots via Markov Games. ICRA 2020: 11338-11344 - [i34]Yutai Hou, Wanxiang Che, Yongkui Lai, Zhihan Zhou, Yijia Liu, Han Liu, Ting Liu:
Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network. CoRR abs/2006.05702 (2020) - [i33]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) - [i32]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) - [i31]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)
2010 – 2019
- 2019
- [j33]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) - [j32]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) - [j31]Carson Eisenach, Han Liu:
Efficient, certifiably optimal clustering with applications to latent variable graphical models. Math. Program. 176(1-2): 137-173 (2019) - [j30]Ethan X. Fang, Han Liu, Mengdi Wang:
Blessing of massive scale: spatial graphical model estimation with a total cardinality constraint approach. Math. Program. 176(1-2): 175-205 (2019) - [j29]Jian Sun, Han Liu, Zian Ma:
Modelling and simulation of highly mixed traffic flow on two-lane two-way urban streets. Simul. Model. Pract. Theory 95: 16-35 (2019) - [j28]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) - [c80]Carson Eisenach, Haichuan Yang, Ji Liu, Han Liu:
Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications. ICLR (Poster) 2019 - [c79]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 - [c78]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 - [i30]Yutai Hou, Zhihan Zhou, Yijia Liu, Ning Wang, Wanxiang Che, Han Liu, Ting Liu:
Few-Shot Sequence Labeling with Label Dependency Transfer. CoRR abs/1906.08711 (2019) - [i29]Xinyang Yi, Zhaoran Wang, Zhuoran Yang, Constantine Caramanis, Han Liu:
More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning. CoRR abs/1907.06257 (2019) - [i28]Boxin Wang, Hengzhi Pei, Han Liu, Bo Li:
AdvCodec: Towards A Unified Framework for Adversarial Text Generation. CoRR abs/1912.10375 (2019) - 2018
- [j27]Amrita Basu, Ritwik Mitra, Han Liu, Stuart L. Schreiber, Paul A. Clemons:
RWEN: response-weighted elastic net for prediction of chemosensitivity of cancer cell lines. Bioinform. 34(19): 3332-3339 (2018) - [j26]Zhuoran Yang, Yang Ning, Han Liu:
On Semiparametric Exponential Family Graphical Models. J. Mach. Learn. Res. 19: 57:1-57:59 (2018) - [j25]Ethan X. Fang, Han Liu, Kim-Chuan Toh, Wen-Xin Zhou:
Max-norm optimization for robust matrix recovery. Math. Program. 167(1): 5-35 (2018) - [j24]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) - [c77]Jason Ge, Zhaoran Wang, Mengdi Wang, Han Liu:
Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems. AISTATS 2018: 1589-1598 - [c76]Daniel R. Jiang, Emmanuel Ekwedike, Han Liu:
Feedback-Based Tree Search for Reinforcement Learning. ICML 2018: 2289-2298 - [c75]Hao Lu, Yuan Cao, Junwei Lu, Han Liu, Zhaoran Wang:
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference. ICML 2018: 3253-3262 - [c74]Qiang Sun, Kean Ming Tan, Han Liu, Tong Zhang:
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation. ICML 2018: 4817-4824 - [c73]Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Basar:
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents. ICML 2018: 5867-5876 - [c72]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 - [c71]Qing Wang, Jiechao Xiong, Lei Han, Peng Sun, Han Liu, Tong Zhang:
Exponentially Weighted Imitation Learning for Batched Historical Data. NeurIPS 2018: 6291-6300 - [c70]Wei Sun, Junwei Lu, Han Liu:
Sketching Method for Large Scale Combinatorial Inference. NeurIPS 2018: 10621-10630 - [i27]Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Basar:
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents. CoRR abs/1802.08757 (2018) - [i26]Daniel R. Jiang, Emmanuel Ekwedike, Han Liu:
Feedback-Based Tree Search for Reinforcement Learning. CoRR abs/1805.05935 (2018) - [i25]Carson Eisenach, Haichuan Yang, Ji Liu, Han Liu:
Marginal Policy Gradients for Complex Control. CoRR abs/1806.05134 (2018) - [i24]Jianqing Fan, Han Liu, Zhaoran Wang, Zhuoran Yang:
Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval. CoRR abs/1808.06996 (2018) - [i23]Chris Junchi Li, Zhaoran Wang, Han Liu:
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes. CoRR abs/1808.09642 (2018) - [i22]Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang:
Diffusion Approximations for Online Principal Component Estimation and Global Convergence. CoRR abs/1808.09645 (2018) - [i21]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) - [i20]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) - [i19]Chao-Bing Song, Ji Liu, Han Liu, Yong Jiang, Tong Zhang:
Fully Implicit Online Learning. CoRR abs/1809.09350 (2018) - [i18]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) - [i17]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) - 2017
- [j23]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) - [j22]Junwei Lu, Mladen Kolar, Han Liu:
Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models. J. Mach. Learn. Res. 18: 203:1-203:78 (2017) - [j21]Mengdi Wang, Ethan X. Fang, Han Liu:
Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions. Math. Program. 161(1-2): 419-449 (2017) - [c69]Zhuoran Yang, Krishnakumar Balasubramanian, Han Liu:
High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation. ICML 2017: 3851-3860 - [c68]Haotian Pang, Han Liu, Robert J. Vanderbei, Tuo Zhao:
Parametric Simplex Method for Sparse Learning. NIPS 2017: 188-197 - [c67]Zhuoran Yang, Krishnakumar Balasubramanian, Zhaoran Wang, Han Liu:
Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein's Lemma. NIPS 2017: 6097-6106 - [i16]Haotian Pang, Tuo Zhao, Robert J. Vanderbei, Han Liu:
Homotopy Parametric Simplex Method for Sparse Learning. CoRR abs/1704.01079 (2017) - [i15]Ari Seff, Alex Beatson, Daniel Suo, Han Liu:
Continual Learning in Generative Adversarial Nets. CoRR abs/1705.08395 (2017) - 2016
- [j20]Robert J. Vanderbei, Kevin Lin, Han Liu, Lie Wang:
Revisiting compressed sensing: exploiting the efficiency of simplex and sparsification methods. Math. Program. Comput. 8(3): 253-269 (2016) - [c66]Yan Li, Han Liu, Warren B. Powell:
A Lasso-based Sparse Knowledge Gradient Policy for Sequential Optimal Learning. AISTATS 2016: 417-425 - [c65]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 - [c64]Quanquan Gu, Zhaoran Wang, Han Liu:
Low-Rank and Sparse Structure Pursuit via Alternating Minimization. AISTATS 2016: 600-609 - [c63]Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Jarvis D. Haupt:
Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning. ICML 2016: 917-925 - [c62]Zhaoran Wang, Quanquan Gu, Han Liu:
On the Statistical Limits of Convex Relaxations. ICML 2016: 1368-1377 - [c61]Zhuoran Yang, Zhaoran Wang, Han Liu, Yonina C. Eldar, Tong Zhang:
Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity. ICML 2016: 2472-2481 - [c60]Houping Xiao, Jing Gao, Zhaoran Wang, Shiyu Wang, Lu Su, Han Liu:
A Truth Discovery Approach with Theoretical Guarantee. KDD 2016: 1925-1934 - [c59]Alex Beatson, Zhaoran Wang, Han Liu:
Blind Attacks on Machine Learners. NIPS 2016: 2397-2405 - [c58]Matey Neykov, Zhaoran Wang, Han Liu:
Agnostic Estimation for Misspecified Phase Retrieval Models. NIPS 2016: 4089-4097 - [c57]Xinyang Yi, Zhaoran Wang, Zhuoran Yang, Constantine Caramanis, Han Liu:
More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning. NIPS 2016: 4475-4483 - [c56]Chris Junchi Li, Zhaoran Wang, Han Liu:
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes. NIPS 2016: 4961-4969 - [i14]Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Jarvis D. Haupt:
Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning. CoRR abs/1605.02711 (2016) - [i13]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) - [i12]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) - [i11]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
- [j19]Li Chen, Han Liu, Jean-Pierre A. Kocher, Hongzhe Li, Jun Chen:
glmgraph: an R package for variable selection and predictive modeling of structured genomic data. Bioinform. 31(24): 3991-3993 (2015) - [j18]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) - [j17]Han Liu, Lie Wang, Tuo Zhao:
Calibrated multivariate regression with application to neural semantic basis discovery. J. Mach. Learn. Res. 16: 1579-1606 (2015) - [j16]Fang Han, Huanran Lu, Han Liu:
A direct estimation of high dimensional stationary vector autoregressions. J. Mach. Learn. Res. 16: 3115-3150 (2015) - [j15]Ethan X. Fang, Bingsheng He, Han Liu, Xiaoming Yuan:
Generalized alternating direction method of multipliers: new theoretical insights and applications. Math. Program. Comput. 7(2): 149-187 (2015) - [j14]Mladen Kolar, Han Liu:
Optimal Feature Selection in High-Dimensional Discriminant Analysis. IEEE Trans. Inf. Theory 61(2): 1063-1083 (2015) - [c55]Huitong Qiu, Sheng Xu, Fang Han, Han Liu, Brian Caffo:
Robust Estimation of Transition Matrices in High Dimensional Heavy-tailed Vector Autoregressive Processes. ICML 2015: 1843-1851 - [c54]Huitong Qiu, Fang Han, Han Liu, Brian Caffo:
Robust Portfolio Optimization. NIPS 2015: 46-54 - [c53]Tuo Zhao, Zhaoran Wang, Han Liu:
A Nonconvex Optimization Framework for Low Rank Matrix Estimation. NIPS 2015: 559-567 - [c52]Wei Sun, Zhaoran Wang, Han Liu, Guang Cheng:
Non-convex Statistical Optimization for Sparse Tensor Graphical Model. NIPS 2015: 1081-1089 - [c51]Xinyang Yi, Zhaoran Wang, Constantine Caramanis, Han Liu:
Optimal Linear Estimation under Unknown Nonlinear Transform. NIPS 2015: 1549-1557 - [c50]Daniel Vainsencher, Han Liu, Tong Zhang:
Local Smoothness in Variance Reduced Optimization. NIPS 2015: 2179-2187 - [c49]Zhaoran Wang, Quanquan Gu, Yang Ning, Han Liu:
High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality. NIPS 2015: 2521-2529 - [i10]Yan Li, Han Liu, Warren B. Powell:
The Knowledge Gradient Policy Using A Sparse Additive Belief Model. CoRR abs/1503.05567 (2015) - [i9]Xinyang Yi, Zhaoran Wang, Constantine Caramanis, Han Liu:
Optimal linear estimation under unknown nonlinear transform. CoRR abs/1505.03257 (2015) - [i8]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
- [j13]Haotian Pang, Han Liu, Robert J. Vanderbei:
The fastclime package for linear programming and large-scale precision matrix estimation in R. J. Mach. Learn. Res. 15(1): 489-493 (2014) - [j12]Mladen Kolar, Han Liu, Eric P. Xing:
Graph estimation from multi-attribute data. J. Mach. Learn. Res. 15(1): 1713-1750 (2014) - [j11]Fang Han, Han Liu:
High Dimensional Semiparametric Scale-Invariant Principal Component Analysis. IEEE Trans. Pattern Anal. Mach. Intell. 36(10): 2016-2032 (2014) - [j10]Bingsheng He, Han Liu, Zhaoran Wang, Xiaoming Yuan:
A Strictly Contractive Peaceman-Rachford Splitting Method for Convex Programming. SIAM J. Optim. 24(3): 1011-1040 (2014) - [j9]Xiaoye Jiang, Yuan Yao, Han Liu, Leonidas J. Guibas:
Compressive Network Analysis. IEEE Trans. Autom. Control. 59(11): 2946-2961 (2014) - [j8]Tuo Zhao, Han Liu:
Calibrated Precision Matrix Estimation for High-Dimensional Elliptical Distributions. IEEE Trans. Inf. Theory 60(12): 7874-7887 (2014) - [c48]Juemin Yang, Fang Han, Rafael A. Irizarry, Han Liu:
Context Aware Group Nearest Shrunken Centroids in Large-Scale Genomic Studies. AISTATS 2014: 1051-1059 - [c47]Han Liu, Lie Wang, Tuo Zhao:
Multivariate Regression with Calibration. NIPS 2014: 127-135 - [c46]Chao Chen, Han Liu, Dimitris N. Metaxas, Tianqi Zhao:
Mode Estimation for High Dimensional Discrete Tree Graphical Models. NIPS 2014: 1323-1331 - [c45]Quanquan Gu, Zhaoran Wang, Han Liu:
Sparse PCA with Oracle Property. NIPS 2014: 1529-1537 - [c44]Tuo Zhao, Mo Yu, Yiming Wang, Raman Arora, Han Liu:
Accelerated Mini-batch Randomized Block Coordinate Descent Method. NIPS 2014: 3329-3337 - [c43]Zhaoran Wang, Huanran Lu, Han Liu:
Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time. NIPS 2014: 3383-3391 - [i7]Zhaoran Wang, Huanran Lu, Han Liu:
Nonconvex Statistical Optimization: Minimax-Optimal Sparse PCA in Polynomial Time. CoRR abs/1408.5352 (2014) - [i6]Tuo Zhao, Han Liu, Tong Zhang:
Pathwise Coordinate Optimization for Sparse Learning: Algorithm and Theory. CoRR abs/1412.7477 (2014) - 2013
- [j7]Fang Han, Tuo Zhao, Han Liu:
CODA: high dimensional copula discriminant analysis. J. Mach. Learn. Res. 14(1): 629-671 (2013) - [c42]Zhaoran Wang, Fang Han, Han Liu:
Sparse Principal Component Analysis for High Dimensional Multivariate Time Series. AISTATS 2013: 48-56 - [c41]Mladen Kolar, Han Liu, Eric P. Xing:
Markov Network Estimation From Multi-attribute Data. ICML (3) 2013: 73-81 - [c40]Fang Han, Han Liu:
Transition Matrix Estimation in High Dimensional Time Series. ICML (2) 2013: 172-180 - [c39]Fang Han, Han Liu:
Principal Component Analysis on non-Gaussian Dependent Data. ICML (1) 2013: 240-248 - [c38]Mladen Kolar, Han Liu:
Feature Selection in High-Dimensional Classification. ICML (1) 2013: 329-337 - [c37]Fang Han, Han Liu:
Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model. NIPS 2013: 1941-1949 - [c36]Tuo Zhao, Han Liu:
Sparse Inverse Covariance Estimation with Calibration. NIPS 2013: 2274-2282 - [i5]Han Liu, Lie Wang, Tuo Zhao:
Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery. CoRR abs/1305.2238 (2013) - [i4]Robert J. Vanderbei, Han Liu, Lie Wang, Kevin Lin:
Optimization for Compressed Sensing: the Simplex Method and Kronecker Sparsification. CoRR abs/1312.4426 (2013) - 2012
- [j6]Indranil Bose, Han Liu, Alex Ye:
Teaching Case: Implementation of an Interorganizational System: The Case of Medical Insurance E-Clearance. J. Inf. Syst. Educ. 23(1): 29-40 (2012) - [j5]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) - [c35]Han Liu, Fang Han, Ming Yuan, John D. Lafferty, Larry A. Wasserman:
High Dimensional Semiparametric Gaussian Copula Graphical Models. ICML 2012 - [c34]Tuo Zhao, Kathryn Roeder, Han Liu:
Smooth-projected Neighborhood Pursuit for High-dimensional Nonparanormal Graph Estimation. NIPS 2012: 162-170 - [c33]Fang Han, Han Liu:
Semiparametric Principal Component Analysis. NIPS 2012: 171-179 - [c32]Fang Han, Han Liu:
Transelliptical Component Analysis. NIPS 2012: 368-376 - [c31]Han Liu, Fang Han, Cun-Hui Zhang:
Transelliptical Graphical Models. NIPS 2012: 809-817 - [c30]Han Liu, John D. Lafferty, Larry A. Wasserman:
Exponential Concentration for Mutual Information Estimation with Application to Forests. NIPS 2012: 2546-2554 - [c29]Xi Chen, Han Liu, Jaime G. Carbonell:
Structured Sparse Canonical Correlation Analysis. AISTATS 2012: 199-207 - [c28]Xiaoye Jiang, Yuan Yao, Han Liu, Leonidas J. Guibas:
Detecting Network Cliques with Radon Basis Pursuit. AISTATS 2012: 565-573 - [c27]Mladen Kolar, Han Liu:
Marginal Regression For Multitask Learning. AISTATS 2012: 647-655 - [c26]Tuo Zhao, Han Liu:
Sparse Additive Machine. AISTATS 2012: 1435-1443 - [i3]John D. Lafferty, Han Liu, Larry A. Wasserman:
Sparse Nonparametric Graphical Models. CoRR abs/1201.0794 (2012) - [i2]Han Liu, Fang Han, Ming Yuan, John D. Lafferty, Larry A. Wasserman:
The Nonparanormal SKEPTIC. CoRR abs/1206.6488 (2012) - 2011
- [j4]Han Liu, Min Xu, Haijie Gu, Anupam Gupta, John D. Lafferty, Larry A. Wasserman:
Forest Density Estimation. J. Mach. Learn. Res. 12: 907-951 (2011) - [i1]Xiaoye Jiang, Yuan Yao, Han Liu, Leonidas J. Guibas:
Compressive Network Analysis. CoRR abs/1104.4605 (2011) - 2010
- [b1]Han Liu:
Nonparametric Learning in High Dimensions. Carnegie Mellon University, USA, 2010 - [j3]Peter Bailey, Ryen W. White, Han Liu, Giridhar Kumaran:
Mining Historic Query Trails to Label Long and Rare Search Engine Queries. ACM Trans. Web 4(4): 15:1-15:27 (2010) - [c25]Xi Chen, Yan Liu, Han Liu, Jaime G. Carbonell:
Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis. AAAI 2010: 425-430 - [c24]Anupam Gupta, John D. Lafferty, Han Liu, Larry A. Wasserman, Min Xu:
Forest Density Estimation. COLT 2010: 394-406 - [c23]Han Liu, Xi Chen, John D. Lafferty, Larry A. Wasserman:
Graph-Valued Regression. NIPS 2010: 1423-1431 - [c22]Han Liu, Kathryn Roeder, Larry A. Wasserman:
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models. NIPS 2010: 1432-1440 - [c21]Han Liu, Xi Chen:
Multivariate Dyadic Regression Trees for Sparse Learning Problems. NIPS 2010: 1441-1449 - [c20]Han Liu, Jian Zhang, Xiaoye Jiang, Jun Liu:
The Group Dantzig Selector. AISTATS 2010: 461-468
2000 – 2009
- 2009
- [j2]Han Liu, John D. Lafferty, Larry A. Wasserman:
The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs. J. Mach. Learn. Res. 10: 2295-2328 (2009) - [c19]Han Liu, Mark Palatucci, Jian Zhang:
Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery. ICML 2009: 649-656 - [c18]Han Liu, Xi Chen:
Nonparametric Greedy Algorithms for the Sparse Learning Problem. NIPS 2009: 1141-1149 - [c17]Han Liu, Jian Zhang:
Estimation Consistency of the Group Lasso and its Applications. AISTATS 2009: 376-383 - [p2]Ji Zhang, Han Liu, Tok Wang Ling, Robert M. Bruckner, A Min Tjoa:
A Framework for Efficient Association Rule Mining in XML Data. Database Technologies: Concepts, Methodologies, Tools, and Applications 2009: 505-526 - 2008
- [c16]Han Liu, John D. Lafferty, Larry A. Wasserman:
Nonparametric regression and classification with joint sparsity constraints. NIPS 2008: 969-976 - 2007
- [c15]Pradeep Ravikumar, Han Liu, John D. Lafferty, Larry A. Wasserman:
SpAM: Sparse Additive Models. NIPS 2007: 1201-1208 - [c14]Han Liu, John D. Lafferty, Larry A. Wasserman:
Sparse Nonparametric Density Estimation in High Dimensions Using the Rodeo. AISTATS 2007: 283-290 - 2006
- [j1]Ji Zhang, Han Liu, Tok Wang Ling, Robert M. Bruckner, A Min Tjoa:
A Framework for Efficient Association Rule Mining in XML Data. J. Database Manag. 17(3): 19-40 (2006) - [c13]Anthony J. Bonner, Han Liu:
Towards the Prediction of Protein Abundance from Tandem Mass Spectrometry Data. SDM 2006: 599-603 - 2005
- [c12]Xiaolin Yang, Feng Jiang, Han Liu, Hongxun Yao, Wen Gao, Chunli Wang:
Visual Sign Language Recognition Based on HMMs and Auto-regressive HMMs. Gesture Workshop 2005: 80-83 - [c11]Sheng Zhang, Ji Zhang, Han Liu, Wei Wang:
XAR-miner: efficient association rules mining for XML data. WWW (Special interest tracks and posters) 2005: 894-895 - [c10]Ji Zhang, Wei Wang, Han Liu, Sheng Zhang:
X-warehouse: building query pattern-driven data. WWW (Special interest tracks and posters) 2005: 896-897 - [p1]Ji Zhang, Han Liu:
D-GridMST: Clustering Large Distributed Spatial Databases. Classification and Clustering for Knowledge Discovery 2005: 61-72 - 2004
- [c9]Ji Zhang, Tok Wang Ling, Robert M. Bruckner, Han Liu:
PC-Filter: A Robust Filtering Technique for Duplicate Record Detection in Large Databases. DEXA 2004: 486-496 - [c8]Ji Zhang, Tok Wang Ling, Robert M. Bruckner, A Min Tjoa, Han Liu:
On Efficient and Effective Association Rule Mining from XML Data. DEXA 2004: 497-507 - [c7]Han Liu, Xiaobin Yuan, Qianying Tang, Rafal Kustra:
An Efficient Method to Estimate Labelled Sample Size for Transductive LDA(QDA/MDA) Based on Bayes Risk. ECML 2004: 274-285 - [c6]Han Liu, Di Wu, Ji Zhang, Xiaolin Yang, Xiaobin Yuan, Rafal Kustra:
Statistical Issues with Labeled Sample Size Analysis for Semi-Supervised Linear Discriminant Analysis. IC-AI 2004: 1007-1012 - [c5]Han Liu, Rafal Kustra, Ji Zhang:
A Novel Dimensionality Reduction Technique Based on Independent Component Analysis for Modeling Microarray Gene Expression Data. IC-AI 2004: 1133-1139 - [c4]Han Liu, Xiaolin Yang, Ji Zhang, Yongji Wang:
Generalized Semi-Infinite Optimization and its Application in Robotics' Path Planning Problem. IC-AI 2004: 1147-1153 - [c3]Ji Zhang, Han Liu:
An Effective and Efficient Data Cleaning Technique in Large Databases. IKE 2004: 501-504 - [c2]Han Liu:
An Extended Linear Strategy Bridging the Gap between Regression and SVD Decomposition for Modeling Peptide Tandem Mass Spectrometry Data. METMBS 2004: 491 - [c1]Han Liu, Qianying Tang, Yongji Wang:
A robot path planning approach based on generalized semi-infinite optimization. RAM 2004: 768-773
Coauthor Index
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