PC-Filter: A Robust Filtering Technique for Duplicate Record Detection in Large Databases. - Ji Zhang 001; Tok Wang Ling; Robert M. Bruckner; Han Liu 001 (DEXA, 2004)

On Efficient and Effective Association Rule Mining from XML Data. - Ji Zhang 001; Tok Wang Ling; Robert M. Bruckner; A Min Tjoa; Han Liu 001 (DEXA, 2004)

An Efficient Method to Estimate Labelled Sample Size for Transductive LDA(QDA/MDA) Based on Bayes Risk. - Han Liu 001; Xiaobin Yuan; Qianying Tang; Rafal Kustra (ECML, 2004)

A Novel Dimensionality Reduction Technique Based on Independent Component Analysis for Modeling Microarray Gene Expression Data. - Han Liu 001; Rafal Kustra; Ji Zhang 001 (IC-AI, 2004)

Statistical Issues with Labeled Sample Size Analysis for Semi-Supervised Linear Discriminant Analysis. - Han Liu 001; Di Wu 050; Ji Zhang 001; Xiaolin Yang; Xiaobin Yuan; Rafal Kustra (IC-AI, 2004)

Generalized Semi-Infinite Optimization and its Application in Robotics' Path Planning Problem. - Han Liu 001; Xiaolin Yang; Ji Zhang 001; Yongji Wang (IC-AI, 2004)

An Effective and Efficient Data Cleaning Technique in Large Databases. - Ji Zhang 001; Han Liu 001 (IKE, 2004)

An Extended Linear Strategy Bridging the Gap between Regression and SVD Decomposition for Modeling Peptide Tandem Mass Spectrometry Data. - Han Liu 001 (METMBS, 2004)

A robot path planning approach based on generalized semi-infinite optimization. - Han Liu 001; Qianying Tang; Yongji Wang (RAM, 2004)

Visual Sign Language Recognition Based on HMMs and Auto-regressive HMMs. - Xiaolin Yang; Feng Jiang 001; Han Liu 001; Hongxun Yao; Wen Gao 001; Chunli Wang (Gesture Workshop, 2005)

X-warehouse: building query pattern-driven data. - Ji Zhang 001; Wei Wang 085; Han Liu 001; Sheng Zhang 010 (WWW (Special interest tracks and posters), 2005)

XAR-miner: efficient association rules mining for XML data. - Sheng Zhang 010; Ji Zhang 001; Han Liu 001; Wei Wang 085 (WWW (Special interest tracks and posters), 2005)

D-GridMST: Clustering Large Distributed Spatial Databases. - Ji Zhang 001; Han Liu 001 (Classification and Clustering for Knowledge Discovery, 2005)

Towards the Prediction of Protein Abundance from Tandem Mass Spectrometry Data. - Anthony J. Bonner; Han Liu 001 (SDM, 2006)

A Framework for Efficient Association Rule Mining in XML Data. - Ji Zhang 001; Han Liu 001; Tok Wang Ling; Robert M. Bruckner; A Min Tjoa (J. Database Manag., 2006)

SpAM: Sparse Additive Models. - Pradeep Ravikumar; Han Liu 001; John D. Lafferty; Larry A. Wasserman (NIPS, 2007)

Sparse Nonparametric Density Estimation in High Dimensions Using the Rodeo. - Han Liu 001; John D. Lafferty; Larry A. Wasserman (AISTATS, 2007)

Nonparametric regression and classification with joint sparsity constraints. - Han Liu 001; John D. Lafferty; Larry A. Wasserman (NIPS, 2008)

A Framework for Efficient Association Rule Mining in XML Data. - Ji Zhang 001; Han Liu 001; Tok Wang Ling; Robert M. Bruckner; A Min Tjoa (Database Technologies: Concepts, Methodologies, Tools, and Applications, 2009)

Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery. - Han Liu 001; Mark Palatucci; Jian Zhang 003 (ICML, 2009)

Nonparametric Greedy Algorithms for the Sparse Learning Problem. - Han Liu 001; Xi Chen 010 (NIPS, 2009)

The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs. - Han Liu 001; John D. Lafferty; Larry A. Wasserman (J. Mach. Learn. Res., 2009)

Estimation Consistency of the Group Lasso and its Applications. - Han Liu 001; Jian Zhang 003 (AISTATS, 2009)

Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis. - Xi Chen 010; Yan Liu 002; Han Liu 001; Jaime G. Carbonell (AAAI, 2010)

Forest Density Estimation. - Anupam Gupta 001; John D. Lafferty; Han Liu 001; Larry A. Wasserman; Min Xu 010 (COLT, 2010)

Multivariate Dyadic Regression Trees for Sparse Learning Problems. - Han Liu 001; Xi Chen 010 (NIPS, 2010)

Graph-Valued Regression. - Han Liu 001; Xi Chen 010; John D. Lafferty; Larry A. Wasserman (NIPS, 2010)

Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models. - Han Liu 001; Kathryn Roeder; Larry A. Wasserman (NIPS, 2010)

The Group Dantzig Selector. - Han Liu 001; Jian Zhang 003; Xiaoye Jiang; Jun Liu 003 (AISTATS, 2010)

Mining Historic Query Trails to Label Long and Rare Search Engine Queries. - Peter Bailey; Ryen W. White; Han Liu 001; Giridhar Kumaran (ACM Trans. Web, 2010)

Nonparametric Learning in High Dimensions. - Han Liu 001 (2010)

Forest Density Estimation. - Han Liu 001; Min Xu 010; Haijie Gu; Anupam Gupta 001; John D. Lafferty; Larry A. Wasserman (J. Mach. Learn. Res., 2011)

High Dimensional Semiparametric Gaussian Copula Graphical Models. - Han Liu 001; Fang Han; Ming Yuan 001; John D. Lafferty; Larry A. Wasserman (ICML, 2012)

Semiparametric Principal Component Analysis. - Fang Han; Han Liu 001 (NIPS, 2012)

Transelliptical Component Analysis. - Fang Han; Han Liu 001 (NIPS, 2012)

Transelliptical Graphical Models. - Han Liu 001; Fang Han; Cun-Hui Zhang (NIPS, 2012)

Exponential Concentration for Mutual Information Estimation with Application to Forests. - Han Liu 001; John D. Lafferty; Larry A. Wasserman (NIPS, 2012)

Smooth-projected Neighborhood Pursuit for High-dimensional Nonparanormal Graph Estimation. - Tuo Zhao; Kathryn Roeder; Han Liu 001 (NIPS, 2012)

The Nonparanormal SKEPTIC. - Han Liu 001; Fang Han; Ming Yuan 001; John D. Lafferty; Larry A. Wasserman (CoRR, 2012)

Sparse Nonparametric Graphical Models - John D. Lafferty; Han Liu 001; Larry A. Wasserman (CoRR, 2012)

Teaching Case: Implementation of an Interorganizational System: The Case of Medical Insurance E-Clearance. - Indranil Bose; Han Liu 001; Alex Ye (J. Inf. Syst. Educ., 2012)

Structured Sparse Canonical Correlation Analysis. - Xi Chen 010; Han Liu 001; Jaime G. Carbonell (AISTATS, 2012)

Detecting Network Cliques with Radon Basis Pursuit. - Xiaoye Jiang; Yuan Yao 011; Han Liu 001; Leonidas J. Guibas (AISTATS, 2012)

Marginal Regression For Multitask Learning. - Mladen Kolar; Han Liu 001 (AISTATS, 2012)

Sparse Additive Machine. - Tuo Zhao; Han Liu 001 (AISTATS, 2012)

The huge Package for High-dimensional Undirected Graph Estimation in R. - Tuo Zhao; Han Liu 001; Kathryn Roeder; John D. Lafferty; Larry A. Wasserman (J. Mach. Learn. Res., 2012)

Sparse Principal Component Analysis for High Dimensional Multivariate Time Series. - Zhaoran Wang 001; Fang Han; Han Liu 001 (AISTATS, 2013)

Transition Matrix Estimation in High Dimensional Time Series. - Fang Han; Han Liu 001 (ICML (2), 2013)

Principal Component Analysis on non-Gaussian Dependent Data. - Fang Han; Han Liu 001 (ICML (1), 2013)

Feature Selection in High-Dimensional Classification. - Mladen Kolar; Han Liu 001 (ICML (1), 2013)

Markov Network Estimation From Multi-attribute Data. - Mladen Kolar; Han Liu 001; Eric P. Xing (ICML (3), 2013)

Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model. - Fang Han; Han Liu 001 (NIPS, 2013)

Sparse Inverse Covariance Estimation with Calibration. - Tuo Zhao; Han Liu 001 (NIPS, 2013)

Optimization for Compressed Sensing: the Simplex Method and Kronecker Sparsification. - Robert J. Vanderbei; Han Liu 001; Lie Wang 002; Kevin Lin (CoRR, 2013)

CODA: high dimensional copula discriminant analysis. - Fang Han; Tuo Zhao; Han Liu 001 (J. Mach. Learn. Res., 2013)

Context Aware Group Nearest Shrunken Centroids in Large-Scale Genomic Studies. - Juemin Yang; Fang Han; Rafael A. Irizarry; Han Liu 001 (AISTATS, 2014)

Mode Estimation for High Dimensional Discrete Tree Graphical Models. - Chao Chen 012; Han Liu 001; Dimitris N. Metaxas; Tianqi Zhao (NIPS, 2014)

Sparse PCA with Oracle Property. - Quanquan Gu; Zhaoran Wang 001; Han Liu 001 (NIPS, 2014)

Multivariate Regression with Calibration. - Han Liu 001; Lie Wang 002; Tuo Zhao (NIPS, 2014)

Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time. - Zhaoran Wang 001; Huanran Lu; Han Liu 001 (NIPS, 2014)

Accelerated Mini-batch Randomized Block Coordinate Descent Method. - Tuo Zhao; Mo Yu; Yiming Wang; Raman Arora; Han Liu 001 (NIPS, 2014)

Nonconvex Statistical Optimization: Minimax-Optimal Sparse PCA in Polynomial Time. - Zhaoran Wang 001; Huanran Lu; Han Liu 001 (CoRR, 2014)

Pathwise Coordinate Optimization for Sparse Learning: Algorithm and Theory. - Tuo Zhao; Han Liu 001; Tong Zhang 001 (CoRR, 2014)

Graph estimation from multi-attribute data. - Mladen Kolar; Han Liu 001; Eric P. Xing (J. Mach. Learn. Res., 2014)

The fastclime package for linear programming and large-scale precision matrix estimation in R. - Haotian Pang; Han Liu 001; Robert J. Vanderbei (J. Mach. Learn. Res., 2014)

High Dimensional Semiparametric Scale-Invariant Principal Component Analysis. - Fang Han; Han Liu 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2014)

A Strictly Contractive Peaceman-Rachford Splitting Method for Convex Programming. - Bingsheng He; Han Liu 001; Zhaoran Wang 001; Xiaoming Yuan 001 (SIAM J. Optim., 2014)

Compressive Network Analysis. - Xiaoye Jiang; Yuan Yao 011; Han Liu 001; Leonidas J. Guibas (IEEE Trans. Autom. Control., 2014)

Calibrated Precision Matrix Estimation for High-Dimensional Elliptical Distributions. - Tuo Zhao; Han Liu 001 (IEEE Trans. Inf. Theory, 2014)

Robust Estimation of Transition Matrices in High Dimensional Heavy-tailed Vector Autoregressive Processes. - Huitong Qiu; Sheng Xu; Fang Han; Han Liu 001; Brian Caffo (ICML, 2015)

Robust Portfolio Optimization. - Huitong Qiu; Fang Han; Han Liu 001; Brian Caffo (NIPS, 2015)

Non-convex Statistical Optimization for Sparse Tensor Graphical Model. - Wei Sun 027; Zhaoran Wang 001; Han Liu 001; Guang Cheng 003 (NIPS, 2015)

Local Smoothness in Variance Reduced Optimization. - Daniel Vainsencher; Han Liu 001; Tong Zhang 001 (NIPS, 2015)

High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality. - Zhaoran Wang 001; Quanquan Gu; Yang Ning; Han Liu 001 (NIPS, 2015)

Optimal Linear Estimation under Unknown Nonlinear Transform. - Xinyang Yi; Zhaoran Wang 001; Constantine Caramanis; Han Liu 001 (NIPS, 2015)

A Nonconvex Optimization Framework for Low Rank Matrix Estimation. - Tuo Zhao; Zhaoran Wang 001; Han Liu 001 (NIPS, 2015)

glmgraph: an R package for variable selection and predictive modeling of structured genomic data. - Li Chen 029; Han Liu 001; Jean-Pierre A. Kocher; Hongzhe Li; Jun Chen (Bioinform., 2015)

The Knowledge Gradient Policy Using A Sparse Additive Belief Model. - Yan Li 013; Han Liu 001; Warren B. Powell (CoRR, 2015)

Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference. - Zhuoran Yang; Zhaoran Wang 001; Han Liu 001; Yonina C. Eldar; Tong Zhang 001 (CoRR, 2015)

A direct estimation of high dimensional stationary vector autoregressions. - Fang Han; Huanran Lu; Han Liu 001 (J. Mach. Learn. Res., 2015)

The flare package for high dimensional linear regression and precision matrix estimation in R. - Xingguo Li; Tuo Zhao; Xiaoming Yuan 001; Han Liu 001 (J. Mach. Learn. Res., 2015)

Calibrated multivariate regression with application to neural semantic basis discovery. - Han Liu 001; Lie Wang 002; Tuo Zhao (J. Mach. Learn. Res., 2015)

Generalized alternating direction method of multipliers: new theoretical insights and applications. - Ethan X. Fang; Bingsheng He; Han Liu 001; Xiaoming Yuan 001 (Math. Program. Comput., 2015)

Optimal Feature Selection in High-Dimensional Discriminant Analysis. - Mladen Kolar; Han Liu 001 (IEEE Trans. Inf. Theory, 2015)

Low-Rank and Sparse Structure Pursuit via Alternating Minimization. - Quanquan Gu; Zhaoran Wang 001; Han Liu 001 (AISTATS, 2016)

A Lasso-based Sparse Knowledge Gradient Policy for Sequential Optimal Learning. - Yan Li 013; Han Liu 001; Warren B. Powell (AISTATS, 2016)

An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization. - Xingguo Li; Tuo Zhao; Raman Arora; Han Liu 001; Mingyi Hong 001 (AISTATS, 2016)

Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning. - Xingguo Li; Tuo Zhao; Raman Arora; Han Liu 001; Jarvis D. Haupt (ICML, 2016)

On the Statistical Limits of Convex Relaxations. - Zhaoran Wang 001; Quanquan Gu; Han Liu 001 (ICML, 2016)

Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity. - Zhuoran Yang; Zhaoran Wang 001; Han Liu 001; Yonina C. Eldar; Tong Zhang 001 (ICML, 2016)

A Truth Discovery Approach with Theoretical Guarantee. - Houping Xiao; Jing Gao 004; Zhaoran Wang 001; Shiyu Wang; Lu Su 001; Han Liu 001 (KDD, 2016)

Blind Attacks on Machine Learners. - Alex Beatson; Zhaoran Wang 001; Han Liu 001 (NIPS, 2016)

Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes. - Chris Junchi Li; Zhaoran Wang 001; Han Liu 001 (NIPS, 2016)

Agnostic Estimation for Misspecified Phase Retrieval Models. - Matey Neykov; Zhaoran Wang 001; Han Liu 001 (NIPS, 2016)

More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning. - Xinyang Yi; Zhaoran Wang 001; Zhuoran Yang; Constantine Caramanis; Han Liu 001 (NIPS, 2016)

A First Order Free Lunch for SQRT-Lasso. - Xingguo Li; Jarvis D. Haupt; Raman Arora; Han Liu 001; Mingyi Hong 001; Tuo Zhao (CoRR, 2016)

Symmetry, Saddle Points, and Global Geometry of Nonconvex Matrix Factorization. - Xingguo Li; Zhaoran Wang 001; Junwei Lu; Raman Arora; Jarvis D. Haupt; Han Liu 001; Tuo Zhao (CoRR, 2016)

Revisiting compressed sensing: exploiting the efficiency of simplex and sparsification methods. - Robert J. Vanderbei; Kevin Lin; Han Liu 001; Lie Wang 002 (Math. Program. Comput., 2016)

High-dimensional Non-Gaussian Single Index Models via Thresholded Score Function Estimation. - Zhuoran Yang; Krishnakumar Balasubramanian 002; Han Liu 001 (ICML, 2017)

Parametric Simplex Method for Sparse Learning. - Haotian Pang; Han Liu 001; Robert J. Vanderbei; Tuo Zhao (NIPS, 2017)

Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein's Lemma. - Zhuoran Yang; Krishnakumar Balasubramanian 002; Zhaoran Wang 001; Han Liu 001 (NIPS, 2017)

Homotopy Parametric Simplex Method for Sparse Learning. - Haotian Pang; Tuo Zhao; Robert J. Vanderbei; Han Liu 001 (CoRR, 2017)

Continual Learning in Generative Adversarial Nets. - Ari Seff; Alex Beatson; Daniel Suo; Han Liu 001 (CoRR, 2017)

On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization. - Xingguo Li; Tuo Zhao; Raman Arora; Han Liu 001; Mingyi Hong 001 (J. Mach. Learn. Res., 2017)

Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models. - Junwei Lu; Mladen Kolar; Han Liu 001 (J. Mach. Learn. Res., 2017)

Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions. - Mengdi Wang; Ethan X. Fang; Han Liu 001 (Math. Program., 2017)

Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems. - Jason Ge; Zhaoran Wang 001; Mengdi Wang; Han Liu 001 (AISTATS, 2018)

Feedback-Based Tree Search for Reinforcement Learning. - Daniel R. Jiang; Emmanuel Ekwedike; Han Liu 001 (ICML, 2018)

The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference. - Hao Lu; Yuan Cao 006; Junwei Lu; Han Liu 001; Zhaoran Wang 001 (ICML, 2018)

Graphical Nonconvex Optimization via an Adaptive Convex Relaxation. - Qiang Sun; Kean Ming Tan; Han Liu 001; Tong Zhang 001 (ICML, 2018)

Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents. - Kaiqing Zhang; Zhuoran Yang; Han Liu 001; Tong Zhang 001; Tamer Basar (ICML, 2018)

Sketching Method for Large Scale Combinatorial Inference. - Wei Sun 027; Junwei Lu; Han Liu 001 (NeurIPS, 2018)

Exponentially Weighted Imitation Learning for Batched Historical Data. - Qing Wang; Jiechao Xiong; Lei Han 001; Peng Sun 011; Han Liu 001; Tong Zhang 001 (NeurIPS, 2018)

RWEN: response-weighted elastic net for prediction of chemosensitivity of cancer cell lines. - Amrita Basu; Ritwik Mitra; Han Liu 001; Stuart L. Schreiber; Paul A. Clemons (Bioinform., 2018)

Marginal Policy Gradients for Complex Control. - Carson Eisenach; Haichuan Yang; Ji Liu 002; Han Liu 001 (CoRR, 2018)

Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval. - Jianqing Fan; Han Liu 001; Zhaoran Wang 001; Zhuoran Yang (CoRR, 2018)

Diffusion Approximations for Online Principal Component Estimation and Global Convergence. - Chris Junchi Li; Mengdi Wang; Han Liu 001; Tong Zhang 001 (CoRR, 2018)

A convex formulation for high-dimensional sparse sliced inverse regression. - Kean Ming Tan; Zhaoran Wang 001; Tong Zhang 001; Han Liu 001; R. Dennis Cook (CoRR, 2018)

TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game. - Peng Sun 011; Xinghai Sun; Lei Han 001; Jiechao Xiong; Qing Wang; Bo Li 064; Yang Zheng; Ji Liu 001; Yongsheng Liu; Han Liu 001; Tong Zhang 001 (CoRR, 2018)

Fully Implicit Online Learning. - Chao-Bing Song; Ji Liu 001; Han Liu 001; Yong Jiang 001; Tong Zhang 001 (CoRR, 2018)

Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space. - Jiechao Xiong; Qing Wang; Zhuoran Yang; Peng Sun 011; Lei Han 001; Yang Zheng; Haobo Fu; Tong Zhang 001; Ji Liu 001; Han Liu 001 (CoRR, 2018)

Finite-Sample Analyses for Fully Decentralized Multi-Agent Reinforcement Learning. - Kaiqing Zhang; Zhuoran Yang; Han Liu 001; Tong Zhang 001; Tamer Basar (CoRR, 2018)

On Semiparametric Exponential Family Graphical Models. - Zhuoran Yang; Yang Ning; Han Liu 001 (J. Mach. Learn. Res., 2018)

Max-norm optimization for robust matrix recovery. - Ethan X. Fang; Han Liu 001; Kim-Chuan Toh; Wen-Xin Zhou (Math. Program., 2018)

Near-optimal stochastic approximation for online principal component estimation. - Chris Junchi Li; Mengdi Wang; Han Liu 001; Tong Zhang 001 (Math. Program., 2018)

Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications. - Carson Eisenach; Haichuan Yang; Ji Liu 002; Han Liu 001 (ICLR (Poster), 2019)

Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI. - Lei Han 001; Peng Sun 011; Yali Du 001; Jiechao Xiong; Qing Wang; Xinghai Sun; Han Liu 001; Tong Zhang 001 (ICML, 2019)

AOMO: An AI-aided Optimizer for Microservices Orchestration. - Xue Leng; Tzung-Han Juang; Yan Chen 004; Han Liu 001 (SIGCOMM Posters and Demos, 2019)

On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don't Worry About its Nonsmooth Loss Function. - Xingguo Li; Haoming Jiang; Jarvis D. Haupt; Raman Arora; Han Liu 001; Mingyi Hong 001; Tuo Zhao (UAI, 2019)

Few-Shot Sequence Labeling with Label Dependency Transfer. - Yutai Hou; Zhihan Zhou 001; Yijia Liu; Ning Wang; Wanxiang Che; Han Liu 001; Ting Liu 001 (CoRR, 2019)

AdvCodec: Towards A Unified Framework for Adversarial Text Generation. - Boxin Wang; Hengzhi Pei; Han Liu 001; Bo Li 026 (CoRR, 2019)

Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python. - Jason Ge; Xingguo Li; Haoming Jiang; Han Liu 001; Tong Zhang 001; Mengdi Wang; Tuo Zhao (J. Mach. Learn. Res., 2019)

Layer-Wise Learning Strategy for Nonparametric Tensor Product Smoothing Spline Regression and Graphical Models. - Kean Ming Tan; Junwei Lu; Tong Zhang 001; Han Liu 001 (J. Mach. Learn. Res., 2019)

Efficient, certifiably optimal clustering with applications to latent variable graphical models. - Carson Eisenach; Han Liu 001 (Math. Program., 2019)

Blessing of massive scale: spatial graphical model estimation with a total cardinality constraint approach. - Ethan X. Fang; Han Liu 001; Mengdi Wang (Math. Program., 2019)

Modelling and simulation of highly mixed traffic flow on two-lane two-way urban streets. - Jian Sun 010; Han Liu 001; Zian Ma (Simul. Model. Pract. Theory, 2019)

Symmetry, Saddle Points, and Global Optimization Landscape of Nonconvex Matrix Factorization. - Xingguo Li; Junwei Lu; Raman Arora; Jarvis D. Haupt; Han Liu 001; Zhaoran Wang 001; Tuo Zhao (IEEE Trans. Inf. Theory, 2019)

Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network. - Yutai Hou; Wanxiang Che; Yongkui Lai; Zhihan Zhou 001; Yijia Liu; Han Liu 001; Ting Liu 001 (ACL, 2020)

Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees. - Binghong Chen; Bo Dai 001; Qinjie Lin; Guo Ye; Han Liu 001; Le Song (ICLR, 2020)

GLAD: Learning Sparse Graph Recovery. - Harsh Shrivastava 001; Xinshi Chen; Binghong Chen; Guanghui Lan; Srinivas Aluru; Han Liu 001; Le Song (ICLR, 2020)

Collision-free Navigation of Human-centered Robots via Markov Games. - Guo Ye; Qinjie Lin; Tzung-Han Juang; Han Liu 001 (ICRA, 2020)

Agnostic Estimation for Phase Retrieval. - Matey Neykov; Zhaoran Wang 001; Han Liu 001 (J. Mach. Learn. Res., 2020)

Tensor Graphical Model: Non-Convex Optimization and Statistical Inference. - Xiang Lyu; Will Wei Sun; Zhaoran Wang 001; Han Liu 001; Jian Yang 002; Guang Cheng 003 (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading. - Zhihan Zhou 001; Liqian Ma; Han Liu 001 (ACL/IJCNLP (Findings), 2021)

RoboFlow: a Data-centric Workflow Management System for Developing AI-enhanced Robots. - Qinjie Lin; Guo Ye; Jiayi Wang; Han Liu 001 (CoRL, 2021)

DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome. - Yanrong Ji; Zhihan Zhou 001; Han Liu 001; Ramana V. Davuluri (Bioinform., 2021)

Finite-Sample Analysis for Decentralized Batch Multiagent Reinforcement Learning With Networked Agents. - Kaiqing Zhang; Zhuoran Yang; Han Liu 001; Tong Zhang 001; Tamer Basar (IEEE Trans. Autom. Control., 2021)

Robust Scatter Matrix Estimation for High Dimensional Distributions With Heavy Tail. - Junwei Lu; Fang Han; Han Liu 001 (IEEE Trans. Inf. Theory, 2021)

Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory. - Zhi Zhang 012; Zhuoran Yang; Han Liu 001; Pratap Tokekar; Furong Huang (ICLR, 2022)

Bregman Proximal Langevin Monte Carlo via Bregman-Moreau Envelopes. - Tim Tsz-Kit Lau; Han Liu 001 (ICML, 2022)

Switch Trajectory Transformer with Distributional Value Approximation for Multi-Task Reinforcement Learning. - Qinjie Lin; Han Liu 001; Biswa Sengupta (CoRR, 2022)

Learning to Infer Belief Embedded Communication. - Guo Ye; Han Liu 001; Biswa Sengupta (CoRR, 2022)

Wasserstein Distributionally Robust Optimization via Wasserstein Barycenters. - Tim Tsz-Kit Lau; Han Liu 001 (CoRR, 2022)

Large-Scale Multi-Document Summarization with Information Extraction and Compression. - Ning Wang; Han Liu 001; Diego Klabjan (CoRR, 2022)

Predicting Drug Repurposing Candidates and Their Mechanisms from A Biomedical Knowledge Graph. - Chunyu Ma; Zhihan Zhou 001; Han Liu 001; David Koslicki (CoRR, 2022)

Interdisciplinary Discovery of Nanomaterials Based on Convolutional Neural Networks. - Tong Xie; Yuwei Wan; Weijian Li 002; Qingyuan Linghu; Shaozhou Wang; Yalun Cai; Han Liu 001; Chunyu Kit; Clara Grazian; Bram Hoex (CoRR, 2022)

Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under Uncertainty. - Zhenyu Pan; Anshujit Sharma; Jerry Yao-Chieh Hu; Zhuo Liu; Ang Li 006; Han Liu 001; Michael C. Huang 001; Tong Geng (AAAI, 2023)

Feature Programming for Multivariate Time Series Prediction. - Alex Daniel Reneau; Jerry Yao-Chieh Hu; Ammar Gilani; Han Liu 001 (ICML, 2023)

EMS®: A Massive Computational Experiment Management System towards Data-driven Robotics. - Qinjie Lin; Guo Ye; Han Liu 001 (ICRA, 2023)

On Sparse Modern Hopfield Model. - Jerry Yao-Chieh Hu; Donglin Yang; Dennis Wu; Chenwei Xu; Bo-Yu Chen; Han Liu 001 (NeurIPS, 2023)

Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms. - Tim Tsz-Kit Lau; Han Liu 001; Thomas Pock (CoRR, 2023)

Feature Programming for Multivariate Time Series Prediction. - Alex Daniel Reneau; Jerry Yao-Chieh Hu; Chenwei Xu; Weijian Li 002; Ammar Gilani; Han Liu 001 (CoRR, 2023)

DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome. - Zhihan Zhou 001; Yanrong Ji; Weijian Li 002; Pratik Dutta; Ramana V. Davuluri; Han Liu 001 (CoRR, 2023)

Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e. - 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; et al. (CoRR, 2023)

Multivariate Time Series Forecasting By Graph Attention Networks With Theoretical Guarantees. - Zhi Zhang 012; Weijian Li 002; Han Liu 001 (AISTATS, 2024)

DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genomes. - Zhihan Zhou 001; Yanrong Ji; Weijian Li 002; Pratik Dutta; Ramana V. Davuluri; Han Liu 001 (ICLR, 2024)

STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction. - Dennis Wu; Jerry Yao-Chieh Hu; Weijian Li 002; Bo-Yu Chen; Han Liu 001 (ICLR, 2024)

Outlier-Efficient Hopfield Layers for Large Transformer-Based Models. - Jerry Yao-Chieh Hu; Pei-Hsuan Chang; Haozheng Luo; Hong-Yu Chen; Weijian Li 002; Wei-Po Wang; Han Liu 001 (ICML, 2024)

On Computational Limits of Modern Hopfield Models: A Fine-Grained Complexity Analysis. - Jerry Yao-Chieh Hu; Thomas Lin; Zhao Song 002; Han Liu 001 (ICML, 2024)

Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models. - Dennis Wu; Jerry Yao-Chieh Hu; Teng-Yun Hsiao; Han Liu 001 (ICML, 2024)

BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model. - Chenwei Xu; Yu-Chao Huang; Jerry Yao-Chieh Hu; Weijian Li 002; Ammar Gilani; Hsi-Sheng Goan; Han Liu 001 (ICML, 2024)

DOS®: A Deployment Operating System for Robots. - Guo Ye; Qinjie Lin; Zening Luo; Han Liu 001 (ICRA, 2024)

ML-Based Real-Time Control at the Edge: An Approach Using hls4ml. - Rui Shi; Seda Ogrenci; J. M. Arnold; J. R. Berlioz; Pierrick Hanlet; Kyle J. Hazelwood; M. A. Ibrahim; Han Liu 001; V. P. Nagaslaev; Aakaash Narayanan; D. J. Nicklaus; Jovan Mitrevski; et al. (IPDPS (Workshops), 2024)

Provably Optimal Memory Capacity for Modern Hopfield Models: Transformer-Compatible Dense Associative Memories as Spherical Codes. - Jerry Yao-Chieh Hu; Dennis Wu; Han Liu 001 (NeurIPS, 2024)

On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs). - Jerry Yao-Chieh Hu; Weimin Wu; Zhuoru Li; Sophia Pi; Zhao Song 002; Han Liu 001 (NeurIPS, 2024)

sEMG-vision Tra: A Gesture Recognition Method Based on Surface EMG Signal-Vision Fusion. - Weili Ding; Shuhui Zhang; Han Liu 001 (SOLI, 2024)

DNABERT-S: Learning Species-Aware DNA Embedding with Genome Foundation Models. - Zhihan Zhou 001; Weimin Wu; Harrison Ho; Jiayi Wang; Lizhen Shi; Ramana V. Davuluri; Zhong Wang; Han Liu 001 (CoRR, 2024)

AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods. - Tim Tsz-Kit Lau; Han Liu 001; Mladen Kolar (CoRR, 2024)

USE: Dynamic User Modeling with Stateful Sequence Models. - Zhihan Zhou 001; Qixiang Fang; Leonardo Neves; Francesco Barbieri; Yozen Liu; Han Liu 001; Maarten W. Bos; Ron Dotsch (CoRR, 2024)

Outlier-Efficient Hopfield Layers for Large Transformer-Based Models. - Jerry Yao-Chieh Hu; Pei-Hsuan Chang; Robin Luo; Hong-Yu Chen; Weijian Li 002; Wei-Po Wang; Han Liu 001 (CoRR, 2024)

Nonparametric Modern Hopfield Models. - Jerry Yao-Chieh Hu; Bo-Yu Chen; Dennis Wu; Feng Ruan; Han Liu 001 (CoRR, 2024)

HeteGraph-Mamba: Heterogeneous Graph Learning via Selective State Space Model. - Zhenyu Pan; Yoonsung Jeong; Xiaoda Liu; Han Liu 001 (CoRR, 2024)

Conv-CoA: Improving Open-domain Question Answering in Large Language Models via Conversational Chain-of-Action. - Zhenyu Pan; Haozheng Luo; Manling Li; Han Liu 001 (CoRR, 2024)

Enhancing Jailbreak Attack Against Large Language Models through Silent Tokens. - Jiahao Yu 001; Haozheng Luo; Jerry Yao-Chieh Hu; Wenbo Guo 002; Han Liu 001; Xinyu Xing 001 (CoRR, 2024)

Decoupled Alignment for Robust Plug-and-Play Adaptation. - Haozheng Luo; Jiahao Yu 001; Wenxin Zhang; Jialong Li; Jerry Yao-Chieh Hu; Xinyu Xing 001; Han Liu 001 (CoRR, 2024)

Computational Limits of Low-Rank Adaptation (LoRA) for Transformer-Based Models. - Jerry Yao-Chieh Hu; Maojiang Su; En-Jui Kuo; Zhao Song 002; Han Liu 001 (CoRR, 2024)

Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods. - Tim Tsz-Kit Lau; Weijian Li 002; Chenwei Xu; Han Liu 001; Mladen Kolar (CoRR, 2024)

On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs). - Jerry Yao-Chieh Hu; Weimin Wu; Zhuoru Li; Zhao Song 002; Han Liu 001 (CoRR, 2024)

Differentially Private Kernel Density Estimation. - Erzhi Liu; Jerry Yao-Chieh Hu; Alex Daniel Reneau; Zhao Song 002; Han Liu 001 (CoRR, 2024)

Scalable Multi-agent Reinforcement Learning for Factory-wide Dynamic Scheduling. - Jaeyeon Jang; Diego Klabjan; Han Liu 001; Nital S. Patel; Xiuqi Li; Balakrishnan Ananthanarayanan; Husam Dauod; Tzung-Han Juang (CoRR, 2024)

Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion? - Zhenyu Pan; Rongyu Cao; Yongchang Cao; Yingwei Ma; Binhua Li; Fei Huang 002; Han Liu 001; Yongbin Li (CoRR, 2024)

Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayesian Theory. - Zhi Zhang 012; Chris Chow; Yasi Zhang; Yanchao Sun; Haochen Zhang; Eric Hanchen Jiang; Han Liu 001; Furong Huang; Yuchen Cui; Oscar Hernan Madrid Padilla (CoRR, 2024)

On Differentially Private String Distances. - Jerry Yao-Chieh Hu; Erzhi Liu; Han Liu 001; Zhao Song 002; Lichen Zhang 003 (CoRR, 2024)

Transformers are Deep Optimizers: Provable In-Context Learning for Deep Model Training. - Weimin Wu; Maojiang Su; Jerry Yao-Chieh Hu; Zhao Song 002; Han Liu 001 (CoRR, 2024)

AlignAb: Pareto-Optimal Energy Alignment for Designing Nature-Like Antibodies. - Yibo Wen; Chenwei Xu; Jerry Yao-Chieh Hu; Han Liu 001 (CoRR, 2024)

Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty. - Guanlin Liu; Zhihan Zhou 001; Han Liu 001; Lifeng Lai (Trans. Mach. Learn. Res., 2024)

Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory. - Zhi Zhang 012; Chris Chow; Yasi Zhang; Yanchao Sun; Haochen Zhang; Eric Hanchen Jiang; Han Liu 001; Furong Huang; Yuchen Cui; Oscar Hernan Madrid Padilla (AISTATS, 2025)

Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism. - Tim Tsz-Kit Lau; Weijian Li 002; Chenwei Xu; Han Liu 001; Mladen Kolar (CPAL, 2025)

Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models. - Jerry Yao-Chieh Hu; Maojiang Su; En-Jui Kuo; Zhao Song 002; Han Liu 001 (ICLR, 2025)

Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency. - Jerry Yao-Chieh Hu; Wei-Po Wang; Ammar Gilani; Chenyang Li; Zhao Song 002; Han Liu 001 (ICLR, 2025)

On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality. - Jerry Yao-Chieh Hu; Weimin Wu; Yi-Chen Lee; Yu-Chao Huang; Minshuo Chen; Han Liu 001 (ICLR, 2025)

Chain-of-Action: Faithful and Multimodal Question Answering through Large Language Models. - Zhenyu Pan; Haozheng Luo; Manling Li; Han Liu 001 (ICLR, 2025)

Do Code LLMs Understand Design Patterns? - Zhenyu Pan; Xuefeng Song; Yunkun Wang; Rongyu Cao; Binhua Li; Yongbin Li; Han Liu 001 (LLM4Code@ICSE, 2025)

Incorporating Gradients to Rules: Towards Lightweight, Adaptive Provenance-based Intrusion Detection. - Lingzhi Wang 002; Xiangmin Shen; Weijian Li 002; Zhenyuan Li; R. Sekar 001; Han Liu 001; Yan Chen 004 (NDSS, 2025)

Learning multiple coordinated agents under directed acyclic graph constraints. - Jaeyeon Jang; Diego Klabjan; Han Liu 001; Nital S. Patel; Xiuqi Li; Balakrishnan Ananthanarayanan; Husam Dauod; Tzung-Han Juang (Expert Syst. Appl., 2025)

MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the Metaverse. - Zhenyu Pan; Han Liu 001 (CoRR, 2025)

Attention Mechanism, Max-Affine Partition, and Universal Approximation. - Hude Liu; Jerry Yao-Chieh Hu; Zhao Song 002; Han Liu 001 (CoRR, 2025)

Fast and Low-Cost Genomic Foundation Models via Outlier Removal. - Haozheng Luo; Chenghao Qiu; Maojiang Su; Zhihan Zhou 001; Zoe Mehta; Guo Ye; Jerry Yao-Chieh Hu; Han Liu 001 (CoRR, 2025)

GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models. - Haozheng Luo; Chenghao Qiu; Yimin Wang; Shang Wu; Jiahao Yu 001; Han Liu 001; Binghui Wang; Yan Chen 004 (CoRR, 2025)

Minimalist Softmax Attention Provably Learns Constrained Boolean Functions. - Jerry Yao-Chieh Hu; Xiwen Zhang; Maojiang Su; Zhao Song 002; Han Liu 001 (CoRR, 2025)