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2020 – today
- 2024
[j3]Shahin Atakishiyev
, Mohammad Salameh, Hengshuai Yao, Randy Goebel:
Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions. IEEE Access 12: 101603-101625 (2024)- 2023
- [c22]Xing Chen, Dongcui Diao, Hechang Chen, Hengshuai Yao, Haiyin Piao, Zhixiao Sun, Zhiwei Yang, Randy Goebel, Bei Jiang, Yi Chang:
The Sufficiency of Off-Policyness and Soft Clipping: PPO Is Still Insufficient according to an Off-Policy Measure. AAAI 2023: 7078-7086 - [i29]Hengshuai Yao:
A new Gradient TD Algorithm with only One Step-size: Convergence Rate Analysis using L-λ Smoothness. CoRR abs/2307.15892 (2023) - [i28]Hengshuai Yao:
Baird Counterexample Is Solved: with an example of How to Debug a Two-time-scale Algorithm. CoRR abs/2308.09732 (2023) - [i27]Xing Chen, Yijun Liu, Zhaogeng Liu, Hechang Chen, Hengshuai Yao, Yi Chang:
Careful at Estimation and Bold at Exploration. CoRR abs/2308.11348 (2023) - 2022
- [c21]Yangchen Pan, Jincheng Mei, Amir-massoud Farahmand, Martha White, Hengshuai Yao, Mohsen Rohani, Jun Luo:
Understanding and mitigating the limitations of prioritized experience replay. UAI 2022: 1561-1571 - [i26]Hengshuai Yao:
Learning to Accelerate by the Methods of Step-size Planning. CoRR abs/2204.01705 (2022) - [i25]Xing Chen, Dongcui Diao, Hechang Chen, Hengshuai Yao, Jielong Yang, Haiyin Piao, Zhixiao Sun, Bei Jiang, Yi Chang:
Sigmoidally Preconditioned Off-policy Learning: a new exploration method for reinforcement learning. CoRR abs/2205.10047 (2022) - [i24]Dongcui Diao, Hengshuai Yao, Bei Jiang:
Class Interference of Deep Neural Networks. CoRR abs/2211.01370 (2022) - [i23]Hengshuai Yao:
The Vanishing Decision Boundary Complexity and the Strong First Component. CoRR abs/2211.16209 (2022) - 2021
- [j2]Keith G. Mills, Mohammad Salameh, Di Niu, Fred X. Han, Seyed Saeed Changiz Rezaei, Hengshuai Yao, Wei Lu, Shuo Lian, Shangling Jui:
Exploring Neural Architecture Search Space via Deep Deterministic Sampling. IEEE Access 9: 110962-110974 (2021) - [j1]Mi-Young Kim, Shahin Atakishiyev, Housam Khalifa Bashier Babiker, Nawshad Farruque, Randy Goebel, Osmar R. Zaïane, Mohammad H. Motallebi, Juliano Rabelo, Talat Iqba Syed, Hengshuai Yao, Peter Chun:
A Multi-Component Framework for the Analysis and Design of Explainable Artificial Intelligence. Mach. Learn. Knowl. Extr. 3(4): 900-921 (2021) - [c20]Shangtong Zhang, Hengshuai Yao, Shimon Whiteson:
Breaking the Deadly Triad with a Target Network. ICML 2021: 12621-12631 - [i22]Shangtong Zhang, Hengshuai Yao, Shimon Whiteson:
Breaking the Deadly Triad with a Target Network. CoRR abs/2101.08862 (2021) - [i21]Ke Sun, Yi Liu, Yingnan Zhao, Hengshuai Yao, Shangling Jui, Linglong Kong:
Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations. CoRR abs/2109.08776 (2021) - [i20]Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao, Randy Goebel:
Towards safe, explainable, and regulated autonomous driving. CoRR abs/2111.10518 (2021) - [i19]Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao, Randy Goebel:
Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions. CoRR abs/2112.11561 (2021) - 2020
- [c19]Shangtong Zhang, Bo Liu, Hengshuai Yao, Shimon Whiteson:
Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation. ICML 2020: 11204-11213 - [c18]Jun Jin, Nhat M. Nguyen, Nazmus Sakib, Daniel Graves, Hengshuai Yao, Martin Jägersand:
Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans. ICRA 2020: 6979-6985 - [c17]Mennatullah Siam, Naren Doraiswamy, Boris N. Oreshkin, Hengshuai Yao, Martin Jägersand:
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings. IJCAI 2020: 860-867 - [i18]Mennatullah Siam, Naren Doraiswamy, Boris N. Oreshkin, Hengshuai Yao, Martin Jägersand:
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Inputs. CoRR abs/2001.09540 (2020) - [i17]Vincent Liu, Adam White, Hengshuai Yao, Martha White:
Towards a practical measure of interference for reinforcement learning. CoRR abs/2007.03807 (2020) - [i16]Jincheng Mei, Yangchen Pan, Martha White, Amir-massoud Farahmand, Hengshuai Yao:
Beyond Prioritized Replay: Sampling States in Model-Based RL via Simulated Priorities. CoRR abs/2007.09569 (2020) - [i15]Daoming Lyu, Qi Qi, Mohammad Ghavamzadeh, Hengshuai Yao, Tianbao Yang, Bo Liu:
Variance-Reduced Off-Policy Memory-Efficient Policy Search. CoRR abs/2009.06548 (2020)
2010 – 2019
- 2019
- [c16]Shangtong Zhang, Hengshuai Yao:
ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search. AAAI 2019: 5789-5796 - [c15]Shangtong Zhang, Hengshuai Yao:
QUOTA: The Quantile Option Architecture for Reinforcement Learning. AAAI 2019: 5797-5804 - [c14]Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong:
Exploration in the Face of Parametric and Intrinsic Uncertainties. AAMAS 2019: 2117-2119 - [c13]Wei Tu, Peng Liu, Jingyu Zhao, Yi Liu, Linglong Kong, Guodong Li, Bei Jiang, Guangjian Tian, Hengshuai Yao:
M-estimation in Low-Rank Matrix Factorization: A General Framework. ICDM 2019: 568-577 - [c12]Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu:
Distributional Reinforcement Learning for Efficient Exploration. ICML 2019: 4424-4434 - [c11]Yangchen Pan, Hengshuai Yao, Amir-massoud Farahmand, Martha White:
Hill Climbing on Value Estimates for Search-control in Dyna. IJCAI 2019: 3209-3215 - [i14]Borislav Mavrin, Hengshuai Yao, Linglong Kong:
Deep Reinforcement Learning with Decorrelation. CoRR abs/1903.07765 (2019) - [i13]Nazmus Sakib, Hengshuai Yao, Hong Zhang:
Reinforcing Classical Planning for Adversary Driving Scenarios. CoRR abs/1903.08606 (2019) - [i12]Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu:
Distributional Reinforcement Learning for Efficient Exploration. CoRR abs/1905.06125 (2019) - [i11]Yangchen Pan, Hengshuai Yao, Amir-massoud Farahmand, Martha White:
Hill Climbing on Value Estimates for Search-control in Dyna. CoRR abs/1906.07791 (2019) - [i10]Khurram Javed, Hengshuai Yao, Martha White:
Is Fast Adaptation All You Need? CoRR abs/1910.01705 (2019) - [i9]Jun Jin, Nhat M. Nguyen, Nazmus Sakib, Daniel Graves, Hengshuai Yao, Martin Jägersand:
Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans. CoRR abs/1911.03074 (2019) - [i8]Shangtong Zhang, Bo Liu, Hengshuai Yao, Shimon Whiteson:
Provably Convergent Off-Policy Actor-Critic with Function Approximation. CoRR abs/1911.04384 (2019) - [i7]Mennatullah Siam, Naren Doraiswamy, Boris N. Oreshkin, Hengshuai Yao, Martin Jägersand:
One-Shot Weakly Supervised Video Object Segmentation. CoRR abs/1912.08936 (2019) - 2018
- [c10]Donglai Zhu, Hao Chen, Hengshuai Yao, Masoud S. Nosrati, Peyman Yadmellat, Yunfei Zhang:
Practical Issues of Action-Conditioned Next Image Prediction. ITSC 2018: 3150-3155 - [i6]Donglai Zhu, Hao Chen, Hengshuai Yao, Masoud S. Nosrati, Peyman Yadmellat, Yunfei Zhang:
Practical Issues of Action-conditioned Next Image Prediction. CoRR abs/1802.02975 (2018) - [i5]Donglai Zhu, Hengshuai Yao, Bei Jiang, Peng Yu:
Negative Log Likelihood Ratio Loss for Deep Neural Network Classification. CoRR abs/1804.10690 (2018) - [i4]Shangtong Zhang, Borislav Mavrin, Linglong Kong, Bo Liu, Hengshuai Yao:
QUOTA: The Quantile Option Architecture for Reinforcement Learning. CoRR abs/1811.02073 (2018) - [i3]Shangtong Zhang, Hao Chen, Hengshuai Yao:
ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search. CoRR abs/1811.02696 (2018) - 2014
- [c9]Hengshuai Yao, Csaba Szepesvári, Bernardo Ávila Pires, Xinhua Zhang:
Pseudo-MDPs and factored linear action models. ADPRL 2014: 1-9 - [c8]Hengshuai Yao, Csaba Szepesvári, Richard S. Sutton, Joseph Modayil, Shalabh Bhatnagar:
Universal Option Models. NIPS 2014: 990-998 - [c7]Chi-Hoon Lee, Hengshuai Yao, Xu He, Su Han Chan, JieYang Chang, Farzin Maghoul:
Learning to predict trending queries: classification - based. WWW (Companion Volume) 2014: 335-336 - 2013
- [i2]Hengshuai Yao, Dale Schuurmans:
Reinforcement Ranking. CoRR abs/1303.5988 (2013) - 2012
- [c6]Hengshuai Yao, Csaba Szepesvári:
Approximate Policy Iteration with Linear Action Models. AAAI 2012: 1212-1218 - [i1]Hengshuai Yao:
Discovering and Leveraging the Most Valuable Links for Ranking. CoRR abs/1210.1626 (2012)
2000 – 2009
- 2009
- [c5]Hengshuai Yao, Shalabh Bhatnagar, Csaba Szepesvári:
LMS-2: Towards an algorithm that is as cheap as LMS and almost as efficient as RLS. CDC 2009: 1181-1188 - [c4]Hengshuai Yao, Richard S. Sutton, Shalabh Bhatnagar, Diao Dongcui, Csaba Szepesvári:
Multi-Step Dyna Planning for Policy Evaluation and Control. NIPS 2009: 2187-2195 - 2008
- [c3]Hengshuai Yao, Zhi-Qiang Liu:
Preconditioned temporal difference learning. ICML 2008: 1208-1215 - [c2]Hengshuai Yao, Zhi-Qiang Liu:
Minimal Residual Approaches for Policy Evaluation in Large Sparse Markov Chains. ISAIM 2008 - 2006
- [c1]Hengshuai Yao, Diao Dongcui, Zengqi Sun:
Historical Temporal Difference Learning: Some Initial Results. IMSCCS (2) 2006: 678-685
Coauthor Index
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