Hsieh, Ping-Chun

32 publications

ICLR 2026 A Reward-Free Viewpoint on Multi-Objective Reinforcement Learning Ying-Tu Chen, Wei Hung, Bing-Shu Wu, Zhang-Wei Hong, Ping-Chun Hsieh

ICLR 2026 Cross-Domain Policy Optimization via Bellman Consistency and Hybrid Critics Ming-Hong Chen, Kuan-Chen Pan, You-De Huang, Xi Liu, Ping-Chun Hsieh

TMLR 2026 Semi-Supervised Cross-Domain Imitation Learning Li-Min Chu, Kai-Siang Ma, Ming-Hong Chen, Ping-Chun Hsieh

ICLR 2026 Test-Time Alignment for Large Language Models via Textual Model Predictive Control Kuang-Da Wang, Teng-Ruei Chen, Yu Heng Hung, Guo-Xun Ko, Shuoyang Ding, Yueh-Hua Wu, Yu-Chiang Frank Wang, Chao-Han Huck Yang, Wen-Chih Peng, Ping-Chun Hsieh

ICML 2025 Action-Constrained Imitation Learning Chia-Han Yeh, Tse-Sheng Nan, Risto Vuorio, Wei Hung, Hung Yen Wu, Shao-Hua Sun, Ping-Chun Hsieh

ICLR 2025 BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Yu Heng Hung, Kai-Jie Lin, Yu-Heng Lin, Chien-Yi Wang, Cheng Sun, Ping-Chun Hsieh

ICLR 2025 Efficient Action-Constrained Reinforcement Learning via Acceptance-Rejection Method and Augmented MDPs Wei Hung, Shao-Hua Sun, Ping-Chun Hsieh

NeurIPS 2025 Learning Human-like RL Agents Through Trajectory Optimization with Action Quantization Jian-Ting Guo, Yu-Cheng Chen, Ping-Chun Hsieh, Kuo-Hao Ho, Po-Wei Huang, Ti-Rong Wu, I-Chen Wu

ICLRW 2025 Learning from Diverse Experts: Behavior Alignment Through Multi-Objective Inverse Reinforcement Learning Jun-Jie Yang, Qian-You Zhang, Chia-Heng Hsu, Xi Liu, Ping-Chun Hsieh

ACML 2025 Relaxed Transition Kernels Can Cure Underestimation in Adversarial Offline Reinforcement Learning Ziyu Wang, Ping-Chun Hsieh, Yu-Shuen Wang, Yun-Hsuan Lien

ICML 2024 Accelerated Policy Gradient: On the Convergence Rates of the Nesterov Momentum for Reinforcement Learning Yen-Ju Chen, Nai-Chieh Huang, Ching-Pei Lee, Ping-Chun Hsieh

ICMLW 2024 BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL Yu Heng Hung, Kai-Jie Lin, Yu-Heng Lin, Chien-Yi Wang, Ping-Chun Hsieh

ICMLW 2024 Cross-Domain Knowledge Transfer for RL via Preference Consistency Ting-Hsuan Huang, Ping-Chun Hsieh

NeurIPS 2024 Diffusion-Reward Adversarial Imitation Learning Chun-Mao Lai, Hsiang-Chun Wang, Ping-Chun Hsieh, Yu-Chiang Frank Wang, Min-Hung Chen, Shao-Hua Sun

ICML 2024 Enhancing Value Function Estimation Through First-Order State-Action Dynamics in Offline Reinforcement Learning Yun-Hsuan Lien, Ping-Chun Hsieh, Tzu-Mao Li, Yu-Shuen Wang

ECML-PKDD 2024 Offline Imitation of Badminton Player Behavior via Experiential Contexts and Brownian Motion Kuang-Da Wang, Wei-Yao Wang, Ping-Chun Hsieh, Wen-Chih Peng

AAAI 2024 PPO-CLIP Attains Global Optimality: Towards Deeper Understandings of Clipping Nai-Chieh Huang, Ping-Chun Hsieh, Kuo-Hao Ho, I-Chen Wu

ICMLW 2024 Survive on Planet Pandora: Robust Cross-Domain RL Under Distinct State-Action Representations Kuan-Chen Pan, MingHong Chen, Xi Liu, Ping-Chun Hsieh

ICMLW 2023 Accelerated Policy Gradient: On the Nesterov Momentum for Reinforcement Learning Yen-Ju Chen, Nai-Chieh Huang, Ping-Chun Hsieh

AISTATS 2023 Coordinate Ascent for Off-Policy RL with Global Convergence Guarantees Hsin-En Su, Yen-Ju Chen, Ping-Chun Hsieh, Xi Liu

ICMLW 2023 Generating Turn-Based Player Behavior via Experience from Demonstrations Kuang-Da Wang, Wei-Yao Wang, Ping-Chun Hsieh, Wen-Chih Peng

ICLR 2023 Q-Pensieve: Boosting Sample Efficiency of Multi-Objective RL Through Memory Sharing of Q-Snapshots Wei Hung, Bo Kai Huang, Ping-Chun Hsieh, Xi Liu

ICML 2023 Revisiting Domain Randomization via Relaxed State-Adversarial Policy Optimization Yun-Hsuan Lien, Ping-Chun Hsieh, Yu-Shuen Wang

AAAI 2023 Reward-Biased Maximum Likelihood Estimation for Neural Contextual Bandits: A Distributional Learning Perspective Yu-Heng Hung, Ping-Chun Hsieh

ACML 2023 Towards Human-like RL: Taming Non-Naturalistic Behavior in Deep RL via Adaptive Behavioral Costs in 3D Games Kuo-Hao Ho, Ping-Chun Hsieh, Chiu-Chou Lin, You-Ren Lou, Feng-Jian Wang, I-Chen Wu

UAI 2021 Escaping from Zero Gradient: Revisiting Action-Constrained Reinforcement Learning via Frank-Wolfe Policy Optimization Jyun-Li Lin, Wei Hung, Shang-Hsuan Yang, Ping-Chun Hsieh, Xi Liu

NeurIPS 2021 NeurWIN: Neural Whittle Index Network for Restless Bandits via Deep RL Khaled Nakhleh, Santosh Ganji, Ping-Chun Hsieh, I-Hong Hou, Srinivas Shakkottai

NeurIPS 2021 Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization Bing-Jing Hsieh, Ping-Chun Hsieh, Xi Liu

AAAI 2021 Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits Yu-Heng Hung, Ping-Chun Hsieh, Xi Liu, P. R. Kumar

ICML 2020 Exploration Through Reward Biasing: Reward-Biased Maximum Likelihood Estimation for Stochastic Multi-Armed Bandits Xi Liu, Ping-Chun Hsieh, Yu Heng Hung, Anirban Bhattacharya, P. Kumar

NeurIPSW 2020 Rethinking Deep Policy Gradients via State-Wise Policy Improvement Kai-Chun Hu, Ping-Chun Hsieh, Ting Han Wei, I-Chen Wu

ICML 2019 Stay with Me: Lifetime Maximization Through Heteroscedastic Linear Bandits with Reneging Ping-Chun Hsieh, Xi Liu, Anirban Bhattacharya, P R Kumar