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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://dblp1.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)
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Books and Theses
- 2010
[b1]Han Liu:
Nonparametric Learning in High Dimensions. Carnegie Mellon University, USA, 2010
Journal Articles
- 2021
- [j28]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) - [j27]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) - 2020
- [j26]Matey Neykov, Zhaoran Wang, Han Liu:
Agnostic Estimation for Phase Retrieval. J. Mach. Learn. Res. 21: 121:1-121:39 (2020) - [j25]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) - 2019
- [j24]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) - [j23]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) - [j22]Carson Eisenach, Han Liu:
Efficient, certifiably optimal clustering with applications to latent variable graphical models. Math. Program. 176(1-2): 137-173 (2019) - [j21]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) - [j20]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) - 2018
- [j19]Zhuoran Yang, Yang Ning, Han Liu:
On Semiparametric Exponential Family Graphical Models. J. Mach. Learn. Res. 19: 57:1-57:59 (2018) - [j18]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) - [j17]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) - 2017
- [j16]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) - [j15]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) - 2016
- [j14]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) - 2015
- [j13]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) - [j12]Han Liu, Lie Wang, Tuo Zhao:
Calibrated multivariate regression with application to neural semantic basis discovery. J. Mach. Learn. Res. 16: 1579-1606 (2015) - [j11]Fang Han, Huanran Lu, Han Liu:
A direct estimation of high dimensional stationary vector autoregressions. J. Mach. Learn. Res. 16: 3115-3150 (2015) - [j10]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) - 2014
- [j9]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) - [j8]Fang Han, Han Liu:
High Dimensional Semiparametric Scale-Invariant Principal Component Analysis. IEEE Trans. Pattern Anal. Mach. Intell. 36(10): 2016-2032 (2014) - [j7]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) - [j6]Tuo Zhao, Han Liu:
Calibrated Precision Matrix Estimation for High-Dimensional Elliptical Distributions. IEEE Trans. Inf. Theory 60(12): 7874-7887 (2014) - 2013
- [j5]Fang Han, Tuo Zhao, Han Liu:
CODA: high dimensional copula discriminant analysis. J. Mach. Learn. Res. 14(1): 629-671 (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) - 2011
- [j3]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) - 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) - 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)
Conference and Workshop Papers
- 2023
- [c67]Qinjie Lin, Guo Ye, Han Liu:
EMS®: A Massive Computational Experiment Management System towards Data-driven Robotics. ICRA 2023: 9068-9075 - 2022
- [c66]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 - 2021
- [c65]Qinjie Lin, Guo Ye, Jiayi Wang, Han Liu:
RoboFlow: a Data-centric Workflow Management System for Developing AI-enhanced Robots. CoRL 2021: 1789-1794 - 2020
- [c64]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 - [c63]Guo Ye, Qinjie Lin, Tzung-Han Juang, Han Liu:
Collision-free Navigation of Human-centered Robots via Markov Games. ICRA 2020: 11338-11344 - 2019
- [c62]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 - [c61]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 - 2018
- [c60]Jason Ge, Zhaoran Wang, Mengdi Wang, Han Liu:
Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems. AISTATS 2018: 1589-1598 - [c59]Hao Lu, Yuan Cao, Junwei Lu, Han Liu, Zhaoran Wang:
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference. ICML 2018: 3253-3262 - [c58]Qiang Sun, Kean Ming Tan, Han Liu, Tong Zhang:
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation. ICML 2018: 4817-4824 - [c57]Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Basar:
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents. ICML 2018: 5867-5876 - [c56]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 - [c55]Qing Wang, Jiechao Xiong, Lei Han, Peng Sun, Han Liu, Tong Zhang:
Exponentially Weighted Imitation Learning for Batched Historical Data. NeurIPS 2018: 6291-6300 - [c54]Wei Sun, Junwei Lu, Han Liu:
Sketching Method for Large Scale Combinatorial Inference. NeurIPS 2018: 10621-10630 - 2017
- [c53]Zhuoran Yang, Krishnakumar Balasubramanian, Han Liu:
High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation. ICML 2017: 3851-3860 - [c52]Haotian Pang, Han Liu, Robert J. Vanderbei, Tuo Zhao:
Parametric Simplex Method for Sparse Learning. NIPS 2017: 188-197 - [c51]Zhuoran Yang, Krishnakumar Balasubramanian, Zhaoran Wang, Han Liu:
Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein's Lemma. NIPS 2017: 6097-6106 - 2016
- [c50]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 - [c49]Quanquan Gu, Zhaoran Wang, Han Liu:
Low-Rank and Sparse Structure Pursuit via Alternating Minimization. AISTATS 2016: 600-609 - [c48]Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Jarvis D. Haupt:
Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning. ICML 2016: 917-925 - [c47]Zhaoran Wang, Quanquan Gu, Han Liu:
On the Statistical Limits of Convex Relaxations. ICML 2016: 1368-1377 - [c46]Zhuoran Yang, Zhaoran Wang, Han Liu, Yonina C. Eldar, Tong Zhang:
Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity. ICML 2016: 2472-2481 - [c45]Houping Xiao, Jing Gao, Zhaoran Wang, Shiyu Wang, Lu Su, Han Liu:
A Truth Discovery Approach with Theoretical Guarantee. KDD 2016: 1925-1934 - [c44]Alex Beatson, Zhaoran Wang, Han Liu:
Blind Attacks on Machine Learners. NIPS 2016: 2397-2405 - [c43]Matey Neykov, Zhaoran Wang, Han Liu:
Agnostic Estimation for Misspecified Phase Retrieval Models. NIPS 2016: 4089-4097 - [c42]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 - [c41]Chris Junchi Li, Zhaoran Wang, Han Liu:
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes. NIPS 2016: 4961-4969 - 2015
- [c40]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 - [c39]Huitong Qiu, Fang Han, Han Liu, Brian Caffo:
Robust Portfolio Optimization. NIPS 2015: 46-54 - [c38]Tuo Zhao, Zhaoran Wang, Han Liu:
A Nonconvex Optimization Framework for Low Rank Matrix Estimation. NIPS 2015: 559-567 - [c37]Wei Sun, Zhaoran Wang, Han Liu, Guang Cheng:
Non-convex Statistical Optimization for Sparse Tensor Graphical Model. NIPS 2015: 1081-1089 - [c36]Xinyang Yi, Zhaoran Wang, Constantine Caramanis, Han Liu:
Optimal Linear Estimation under Unknown Nonlinear Transform. NIPS 2015: 1549-1557 - [c35]Daniel Vainsencher, Han Liu, Tong Zhang:
Local Smoothness in Variance Reduced Optimization. NIPS 2015: 2179-2187 - [c34]Zhaoran Wang, Quanquan Gu, Yang Ning, Han Liu:
High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality. NIPS 2015: 2521-2529 - 2014
- [c33]Juemin Yang, Fang Han, Rafael A. Irizarry, Han Liu:
Context Aware Group Nearest Shrunken Centroids in Large-Scale Genomic Studies. AISTATS 2014: 1051-1059 - [c32]Han Liu, Lie Wang, Tuo Zhao:
Multivariate Regression with Calibration. NIPS 2014: 127-135 - [c31]Quanquan Gu, Zhaoran Wang, Han Liu:
Sparse PCA with Oracle Property. NIPS 2014: 1529-1537 - [c30]Tuo Zhao, Mo Yu, Yiming Wang, Raman Arora, Han Liu:
Accelerated Mini-batch Randomized Block Coordinate Descent Method. NIPS 2014: 3329-3337 - [c29]Zhaoran Wang, Huanran Lu, Han Liu:
Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time. NIPS 2014: 3383-3391 - 2013
- [c28]Zhaoran Wang, Fang Han, Han Liu:
Sparse Principal Component Analysis for High Dimensional Multivariate Time Series. AISTATS 2013: 48-56 - [c27]Fang Han, Han Liu:
Transition Matrix Estimation in High Dimensional Time Series. ICML (2) 2013: 172-180 - [c26]Fang Han, Han Liu:
Principal Component Analysis on non-Gaussian Dependent Data. ICML (1) 2013: 240-248 - [c25]Fang Han, Han Liu:
Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model. NIPS 2013: 1941-1949 - [c24]Tuo Zhao, Han Liu:
Sparse Inverse Covariance Estimation with Calibration. NIPS 2013: 2274-2282 - 2012
- [c23]Han Liu, Fang Han, Ming Yuan, John D. Lafferty, Larry A. Wasserman:
High Dimensional Semiparametric Gaussian Copula Graphical Models. ICML 2012 - [c22]Tuo Zhao, Kathryn Roeder, Han Liu:
Smooth-projected Neighborhood Pursuit for High-dimensional Nonparanormal Graph Estimation. NIPS 2012: 162-170 - [c21]Fang Han, Han Liu:
Semiparametric Principal Component Analysis. NIPS 2012: 171-179 - [c20]Fang Han, Han Liu:
Transelliptical Component Analysis. NIPS 2012: 368-376 - [c19]Han Liu, Fang Han, Cun-Hui Zhang:
Transelliptical Graphical Models. NIPS 2012: 809-817 - [c18]Han Liu, John D. Lafferty, Larry A. Wasserman:
Exponential Concentration for Mutual Information Estimation with Application to Forests. NIPS 2012: 2546-2554 - [c17]Tuo Zhao, Han Liu:
Sparse Additive Machine. AISTATS 2012: 1435-1443 - 2010
- [c16]Anupam Gupta, John D. Lafferty, Han Liu, Larry A. Wasserman, Min Xu:
Forest Density Estimation. COLT 2010: 394-406 - [c15]Han Liu, Xi Chen, John D. Lafferty, Larry A. Wasserman:
Graph-Valued Regression. NIPS 2010: 1423-1431 - [c14]Han Liu, Kathryn Roeder, Larry A. Wasserman:
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models. NIPS 2010: 1432-1440 - 2008
- [c13]Han Liu, John D. Lafferty, Larry A. Wasserman:
Nonparametric regression and classification with joint sparsity constraints. NIPS 2008: 969-976 - 2007
- [c12]Pradeep Ravikumar, Han Liu, John D. Lafferty, Larry A. Wasserman:
SpAM: Sparse Additive Models. NIPS 2007: 1201-1208 - [c11]Han Liu, John D. Lafferty, Larry A. Wasserman:
Sparse Nonparametric Density Estimation in High Dimensions Using the Rodeo. AISTATS 2007: 283-290 - 2005
- [c10]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 - [c9]Ji Zhang, Wei Wang, Han Liu, Sheng Zhang:
X-warehouse: building query pattern-driven data. WWW (Special interest tracks and posters) 2005: 896-897 - 2004
- [c8]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 - [c7]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 - [c6]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 - [c5]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 - [c4]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 - [c3]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 - [c2]Ji Zhang, Han Liu:
An Effective and Efficient Data Cleaning Technique in Large Databases. IKE 2004: 501-504 - [c1]Han Liu, Qianying Tang, Yongji Wang:
A robot path planning approach based on generalized semi-infinite optimization. RAM 2004: 768-773
Parts in Books or Collections
- 2009
- [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 - 2005
- [p1]Ji Zhang, Han Liu:
D-GridMST: Clustering Large Distributed Spatial Databases. Classification and Clustering for Knowledge Discovery 2005: 61-72
Informal and Other Publications
- 2023
- [i30]Quanquan Gu, Zhaoran Wang, Han Liu:
Sparse PCA with Oracle Property. CoRR abs/2312.16793 (2023) - 2022
- [i29]Qinjie Lin, Han Liu, Biswa Sengupta:
Switch Trajectory Transformer with Distributional Value Approximation for Multi-Task Reinforcement Learning. CoRR abs/2203.07413 (2022) - [i28]Guo Ye, Han Liu, Biswa Sengupta:
Learning to Infer Belief Embedded Communication. CoRR abs/2203.07832 (2022) - 2020
- [i27]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) - [i26]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) - [i25]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) - 2019
- [i24]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) - 2018
- [i23]Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Basar:
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents. CoRR abs/1802.08757 (2018) - [i22]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) - [i21]Chris Junchi Li, Zhaoran Wang, Han Liu:
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes. CoRR abs/1808.09642 (2018) - [i20]Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang:
Diffusion Approximations for Online Principal Component Estimation and Global Convergence. CoRR abs/1808.09645 (2018) - [i19]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) - [i18]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) - [i17]Chao-Bing Song, Ji Liu, Han Liu, Yong Jiang, Tong Zhang:
Fully Implicit Online Learning. CoRR abs/1809.09350 (2018) - [i16]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) - [i15]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
- [i14]Haotian Pang, Tuo Zhao, Robert J. Vanderbei, Han Liu:
Homotopy Parametric Simplex Method for Sparse Learning. CoRR abs/1704.01079 (2017) - [i13]Ari Seff, Alex Beatson, Daniel Suo, Han Liu:
Continual Learning in Generative Adversarial Nets. CoRR abs/1705.08395 (2017) - 2016
- [i12]Xingguo Li, Tuo Zhao, Raman Arora, Han Liu, Jarvis D. Haupt:
Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning. CoRR abs/1605.02711 (2016) - [i11]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) - [i10]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) - [i9]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
- [i8]Xinyang Yi, Zhaoran Wang, Constantine Caramanis, Han Liu:
Optimal linear estimation under unknown nonlinear transform. CoRR abs/1505.03257 (2015) - [i7]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
- [i6]Zhaoran Wang, Huanran Lu, Han Liu:
Nonconvex Statistical Optimization: Minimax-Optimal Sparse PCA in Polynomial Time. CoRR abs/1408.5352 (2014) - [i5]Tuo Zhao, Han Liu, Tong Zhang:
Pathwise Coordinate Optimization for Sparse Learning: Algorithm and Theory. CoRR abs/1412.7477 (2014) - 2013
- [i4]Han Liu, Lie Wang, Tuo Zhao:
Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery. CoRR abs/1305.2238 (2013) - [i3]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
- [i2]John D. Lafferty, Han Liu, Larry A. Wasserman:
Sparse Nonparametric Graphical Models. CoRR abs/1201.0794 (2012) - [i1]Han Liu, Fang Han, Ming Yuan, John D. Lafferty, Larry A. Wasserman:
The Nonparanormal SKEPTIC. CoRR abs/1206.6488 (2012)
Coauthor Index
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