Historical Temporal Difference Learning: Some Initial Results. - Hengshuai Yao; Diao Dongcui; Zengqi Sun (IMSCCS (2), 2006)

Preconditioned temporal difference learning. - Hengshuai Yao; Zhi-Qiang Liu (ICML, 2008)

Minimal Residual Approaches for Policy Evaluation in Large Sparse Markov Chains. - Hengshuai Yao; Zhi-Qiang Liu (ISAIM, 2008)

LMS-2: Towards an algorithm that is as cheap as LMS and almost as efficient as RLS. - Hengshuai Yao; Shalabh Bhatnagar; Csaba Szepesvári (CDC, 2009)

Multi-Step Dyna Planning for Policy Evaluation and Control. - Hengshuai Yao; Richard S. Sutton; Shalabh Bhatnagar; Diao Dongcui; Csaba Szepesvári (NIPS, 2009)

Approximate Policy Iteration with Linear Action Models. - Hengshuai Yao; Csaba Szepesvári (AAAI, 2012)

Discovering and Leveraging the Most Valuable Links for Ranking - Hengshuai Yao (CoRR, 2012)

Reinforcement Ranking - Hengshuai Yao; Dale Schuurmans (CoRR, 2013)

Pseudo-MDPs and factored linear action models. - Hengshuai Yao; Csaba Szepesvári; Bernardo Ávila Pires; Xinhua Zhang (ADPRL, 2014)

Universal Option Models. - Hengshuai Yao; Csaba Szepesvári; Richard S. Sutton; Joseph Modayil; Shalabh Bhatnagar (NIPS, 2014)

Learning to predict trending queries: classification - based. - Chi-Hoon Lee; Hengshuai Yao; Xu He; Su Han Chan; JieYang Chang; Farzin Maghoul (WWW (Companion Volume), 2014)

Practical Issues of Action-Conditioned Next Image Prediction. - Donglai Zhu; Hao Chen; Hengshuai Yao; Masoud S. Nosrati; Peyman Yadmellat; Yunfei Zhang (ITSC, 2018)

Negative Log Likelihood Ratio Loss for Deep Neural Network Classification. - Donglai Zhu; Hengshuai Yao; Bei Jiang; Peng Yu (CoRR, 2018)

QUOTA: The Quantile Option Architecture for Reinforcement Learning. - Shangtong Zhang; Borislav Mavrin; Linglong Kong; Bo Liu 006; Hengshuai Yao (CoRR, 2018)

ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search. - Shangtong Zhang; Hao Chen; Hengshuai Yao (CoRR, 2018)

ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search. - Shangtong Zhang; Hengshuai Yao (AAAI, 2019)

QUOTA: The Quantile Option Architecture for Reinforcement Learning. - Shangtong Zhang; Hengshuai Yao (AAAI, 2019)

Exploration in the Face of Parametric and Intrinsic Uncertainties. - Borislav Mavrin; Shangtong Zhang; Hengshuai Yao; Linglong Kong (AAMAS, 2019)

M-estimation in Low-Rank Matrix Factorization: A General Framework. - Wei Tu; Peng Liu 048; Jingyu Zhao 001; Yi Liu 062; Linglong Kong; Guodong Li; Bei Jiang; Guangjian Tian; Hengshuai Yao (ICDM, 2019)

Distributional Reinforcement Learning for Efficient Exploration. - Borislav Mavrin; Hengshuai Yao; Linglong Kong; Kaiwen Wu; Yaoliang Yu (ICML, 2019)

Hill Climbing on Value Estimates for Search-control in Dyna. - Yangchen Pan; Hengshuai Yao; Amir-massoud Farahmand; Martha White (IJCAI, 2019)

Deep Reinforcement Learning with Decorrelation. - Borislav Mavrin; Hengshuai Yao; Linglong Kong (CoRR, 2019)

Reinforcing Classical Planning for Adversary Driving Scenarios. - Nazmus Sakib; Hengshuai Yao; Hong Zhang (CoRR, 2019)

Distributional Reinforcement Learning for Efficient Exploration. - Borislav Mavrin; Shangtong Zhang; Hengshuai Yao; Linglong Kong; Kaiwen Wu; Yaoliang Yu (CoRR, 2019)

Is Fast Adaptation All You Need? - Khurram Javed; Hengshuai Yao; Martha White (CoRR, 2019)

Provably Convergent Off-Policy Actor-Critic with Function Approximation. - Shangtong Zhang; Bo Liu 006; Hengshuai Yao; Shimon Whiteson (CoRR, 2019)

One-Shot Weakly Supervised Video Object Segmentation. - Mennatullah Siam; Naren Doraiswamy; Boris N. Oreshkin; Hengshuai Yao; Martin Jägersand (CoRR, 2019)

Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation. - Shangtong Zhang; Bo Liu 006; Hengshuai Yao; Shimon Whiteson (ICML, 2020)

Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans. - Jun Jin 001; Nhat M. Nguyen; Nazmus Sakib; Daniel Graves; Hengshuai Yao; Martin Jägersand (ICRA, 2020)

Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings. - Mennatullah Siam; Naren Doraiswamy; Boris N. Oreshkin; Hengshuai Yao; Martin Jägersand (IJCAI, 2020)

Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Inputs. - Mennatullah Siam; Naren Doraiswamy; Boris N. Oreshkin; Hengshuai Yao; Martin Jägersand (CoRR, 2020)

Towards a practical measure of interference for reinforcement learning. - Vincent Liu; Adam White 001; Hengshuai Yao; Martha White (CoRR, 2020)

Beyond Prioritized Replay: Sampling States in Model-Based RL via Simulated Priorities. - Jincheng Mei; Yangchen Pan; Martha White; Amir-massoud Farahmand; Hengshuai Yao (CoRR, 2020)

Variance-Reduced Off-Policy Memory-Efficient Policy Search. - Daoming Lyu; Qi Qi 006; Mohammad Ghavamzadeh; Hengshuai Yao; Tianbao Yang; Bo Liu 006 (CoRR, 2020)

Breaking the Deadly Triad with a Target Network. - Shangtong Zhang; Hengshuai Yao; Shimon Whiteson (ICML, 2021)

Exploring Neural Architecture Search Space via Deep Deterministic Sampling. - Keith G. Mills; Mohammad Salameh; Di Niu; Fred X. Han; Seyed Saeed Changiz Rezaei; Hengshuai Yao; Wei Lu 023; Shuo Lian; Shangling Jui (IEEE Access, 2021)

Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations. - Ke Sun 013; Yi Liu 062; Yingnan Zhao; Hengshuai Yao; Shangling Jui; Linglong Kong (CoRR, 2021)

Towards safe, explainable, and regulated autonomous driving. - Shahin Atakishiyev; Mohammad Salameh; Hengshuai Yao; Randy Goebel (CoRR, 2021)

A Multi-Component Framework for the Analysis and Design of Explainable Artificial Intelligence. - Mi-Young Kim; Shahin Atakishiyev; Housam Khalifa Bashier Babiker; Nawshad Farruque; Randy Goebel; Osmar R. Zaïane; Mohammad H. Motallebi; Juliano Rabelo 001; Talat Iqba Syed; Hengshuai Yao; Peter Chun (Mach. Learn. Knowl. Extr., 2021)

Understanding and mitigating the limitations of prioritized experience replay. - Yangchen Pan; Jincheng Mei; Amir-massoud Farahmand; Martha White; Hengshuai Yao; Mohsen Rohani; Jun Luo 009 (UAI, 2022)

Learning to Accelerate by the Methods of Step-size Planning. - Hengshuai Yao (CoRR, 2022)

Sigmoidally Preconditioned Off-policy Learning: a new exploration method for reinforcement learning. - Xing Chen; Dongcui Diao; Hechang Chen; Hengshuai Yao; Jielong Yang; Haiyin Piao; Zhixiao Sun; Bei Jiang; Yi Chang 001 (CoRR, 2022)

Class Interference of Deep Neural Networks. - Dongcui Diao; Hengshuai Yao; Bei Jiang (CoRR, 2022)

The Vanishing Decision Boundary Complexity and the Strong First Component. - Hengshuai Yao (CoRR, 2022)

The Sufficiency of Off-Policyness and Soft Clipping: PPO Is Still Insufficient according to an Off-Policy Measure. - Xing Chen; Dongcui Diao; Hechang Chen; Hengshuai Yao; Haiyin Piao; Zhixiao Sun; Zhiwei Yang 005; Randy Goebel; Bei Jiang; Yi Chang 001 (AAAI, 2023)

A new Gradient TD Algorithm with only One Step-size: Convergence Rate Analysis using L-λ Smoothness. - Hengshuai Yao (CoRR, 2023)

Baird Counterexample Is Solved: with an example of How to Debug a Two-time-scale Algorithm. - Hengshuai Yao (CoRR, 2023)

Careful at Estimation and Bold at Exploration. - Xing Chen; Yijun Liu; Zhaogeng Liu; Hechang Chen; Hengshuai Yao; Yi Chang 001 (CoRR, 2023)

Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions. - Shahin Atakishiyev; Mohammad Salameh; Hengshuai Yao; Randy Goebel (IEEE Access, 2024)