Zhang-Wei Hong

70 papers A* 18A 3Misc 1Journal 44Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Pushing Forward Pareto Frontiers of Proactive Agents with Behavioral Agentic Optimization. CoRR Yihang Yao, Zhepeng Cen, Haohong Lin, Shiqi Liu, Zuxin Liu, Jiacheng Zhu, Zhang-Wei Hong, Laixi Shi, Ding Zhao
2025	J	`jnl`	A distributional reinforcement learning model for optimal glucose control after cardiac surgery. npj Digit. Medicine Jacob Desman, Zhang-Wei Hong, Moein Sabounchi, Ashwin S. Sawant, Jaskirat Gill, Ana C. Costa, Gagan Kumar, Rajeev Sharma, Arpeta Gupta, Paul McCarthy, Veena Nandwani, Doug Powell, Alexandra Carideo, Donnie Goodwin, Sanam Ahmed, Umesh Gidwani, Matthew A. Levin, Robin Varghese, Farzan Filsoufi, Robert Freeman, Avniel Shetreat-Klein, Alexander W. Charney, Ira S. Hofer, Lili Chan, David Reich, Patricia H. Kovatch, Roopa Kohli-Seth, Monica Kraft, Pulkit Agrawal, John A. Kellum, Girish N. Nadkarni, Ankit Sakhuja
2025	J	`jnl`	BOAD: Discovering Hierarchical Software Engineering Agents via Bandit Optimization. CoRR Iris Xu, Guangtao Zeng, Zexue He, Charles Jin, Aldo Pareja, Dan Gutfreund, Chuang Gan, Zhang-Wei Hong
2025	J	`jnl`	Composition-Grounded Instruction Synthesis for Visual Reasoning. CoRR Xinyi Gu, Jiayuan Mao, Zhang-Wei Hong, Zhuoran Yu, Pengyuan Li, Dhiraj Joshi, Rogério Feris, Zexue He
2025	J	`jnl`	GPTOpt: Towards Efficient LLM-Based Black-Box Optimization. CoRR Jamison Meindl, Yunsheng Tian, Tony Cui, Veronika Thost, Zhang-Wei Hong, Jie Chen, Wojciech Matusik, Mina Konakovic-Lukovic
2025	J	`jnl`	Going Beyond Heuristics by Imposing Policy Improvement as a Constraint. CoRR Chi-Chang Lee, Zhang-Wei Hong, Pulkit Agrawal
2025	A*	`conf`	ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization. ICLR Chen Bo Calvin Zhang, Zhang-Wei Hong, Aldo Pacchiano, Pulkit Agrawal
2025	J	`jnl`	RL Tango: Reinforcing Generator and Verifier Together for Language Reasoning. CoRR Kaiwen Zha, Zhengqi Gao, Maohao Shen, Zhang-Wei Hong, Duane S. Boning, Dina Katabi
2025	A*	`conf`	ReGen: Generative Robot Simulation via Inverse Design. ICLR Phat Nguyen, Tsun-Hsuan Wang, Zhang-Wei Hong, Erfan Aasi, Andrew Silva, Guy Rosman, Sertac Karaman, Daniela Rus
2025	J	`jnl`	ReGen: Generative Robot Simulation via Inverse Design. CoRR Phat Nguyen, Tsun-Hsuan Wang, Zhang-Wei Hong, Erfan Aasi, Andrew Silva, Guy Rosman, Sertac Karaman, Daniela Rus
2025	J	`jnl`	Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software Engineering. CoRR Guangtao Zeng, Maohao Shen, Delin Chen, Zhenting Qi, Subhro Das, Dan Gutfreund, David Cox, Gregory W. Wornell, Wei Lu, Zhang-Wei Hong, Chuang Gan
2025	A*	`conf`	Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search. ICML Maohao Shen, Guangtao Zeng, Zhenting Qi, Zhang-Wei Hong, Zhenfang Chen, Wei Lu, Gregory W. Wornell, Subhro Das, David Daniel Cox, Chuang Gan
2025	J	`jnl`	Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search. CoRR Maohao Shen, Guangtao Zeng, Zhenting Qi, Zhang-Wei Hong, Zhenfang Chen, Wei Lu, Gregory W. Wornell, Subhro Das, David Cox, Chuang Gan
2025	J	`jnl`	Tailored Primitive Initialization is the Secret Key to Reinforcement Learning. CoRR Yihang Yao, Guangtao Zeng, Raina Wu, Yang Zhang, Ding Zhao, Zhang-Wei Hong, Chuang Gan
2025	J	`jnl`	Your Reward Function for RL is Your Best PRM for Search: Unifying RL and Search-Based TTS. CoRR Can Jin, Yang Zhou, Qixin Zhang, Hongwu Peng, Di Zhang, Marco Pavone, Ligong Han, Zhang-Wei Hong, Tong Che, Dimitris N. Metaxas
2025	J	`jnl`	ZeroShotOpt: Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization. CoRR Jamison Meindl, Yunsheng Tian, Tony Cui, Veronika Thost, Zhang-Wei Hong, Johannes Dürholt, Jie Chen, Wojciech Matusik, Mina Konakovic-Lukovic
2024	J	`jnl`	Composing Synergistic Macro Actions for Reinforcement Learning Agents. IEEE Trans. Neural Networks Learn. Syst. Yu-Ming Chen, Kuan-Yu Chang, Chien Liu, Tsu-Ching Hsiao, Zhang-Wei Hong, Chun-Yi Lee
2024	A*	`conf`	Curiosity-driven Red-teaming for Large Language Models. ICLR Zhang-Wei Hong, Idan Shenfeld, Tsun-Hsuan Wang, Yung-Sung Chuang, Aldo Pareja, James R. Glass, Akash Srivastava, Pulkit Agrawal
2024	J	`jnl`	Curiosity-driven Red-teaming for Large Language Models. CoRR Zhang-Wei Hong, Idan Shenfeld, Tsun-Hsuan Wang, Yung-Sung Chuang, Aldo Pareja, James R. Glass, Akash Srivastava, Pulkit Agrawal
2024	J	`jnl`	Embodied Red Teaming for Auditing Robotic Foundation Models. CoRR Sathwik Karnik, Zhang-Wei Hong, Nishant Abhangi, Yen-Chen Lin, Tsun-Hsuan Wang, Pulkit Agrawal
2024	A*	`conf`	Going Beyond Heuristics by Imposing Policy Improvement as a Constraint. NeurIPS Chi-Chang Lee, Zhang-Wei Hong, Pulkit Agrawal
2024	J	`jnl`	Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building. Trans. Mach. Learn. Res. Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila R. Fiete
2024	J	`jnl`	ImageNet-RIB Benchmark: Large Pre-Training Datasets Don't Guarantee Robustness after Fine-Tuning. CoRR Jaedong Hwang, Brian Cheung, Zhang-Wei Hong, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete
2024	A*	`conf`	Maximizing Quadruped Velocity by Minimizing Energy. ICRA Srinath Mahankali, Chi-Chang Lee, Gabriel B. Margolis, Zhang-Wei Hong, Pulkit Agrawal
2024	J	`jnl`	ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization. CoRR Chen Bo Calvin Zhang, Zhang-Wei Hong, Aldo Pacchiano, Pulkit Agrawal
2024	J	`jnl`	ROER: Regularized Optimal Experience Replay. RLJ Changling Li, Zhang-Wei Hong, Pulkit Agrawal, Divyansh Garg, Joni Pajarinen
2024	J	`jnl`	ROER: Regularized Optimal Experience Replay. CoRR Changling Li, Zhang-Wei Hong, Pulkit Agrawal, Divyansh Garg, Joni Pajarinen
2024	A*	`conf`	Random Latent Exploration for Deep Reinforcement Learning. ICML Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, Pulkit Agrawal
2024	J	`jnl`	Random Latent Exploration for Deep Reinforcement Learning. CoRR Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, Pulkit Agrawal
2024	A	`conf`	Text-to-Drive: Diverse Driving Behavior Synthesis via Large Language Models. IROS Phat Nguyen, Tsun-Hsuan Wang, Zhang-Wei Hong, Sertac Karaman, Daniela Rus
2024	J	`jnl`	Text-to-Drive: Diverse Driving Behavior Synthesis via Large Language Models. CoRR Phat Nguyen, Tsun-Hsuan Wang, Zhang-Wei Hong, Sertac Karaman, Daniela Rus
2023	A*	`conf`	Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets. NeurIPS Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik, Abhishek Bhandwaldar, Akash Srivastava, Joni Pajarinen, Romain Laroche, Abhishek Gupta, Pulkit Agrawal
2023	J	`jnl`	Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets. CoRR Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik, Abhishek Bhandwaldar, Akash Srivastava, Joni Pajarinen, Romain Laroche, Abhishek Gupta, Pulkit Agrawal
2023	J	`jnl`	Decentralized Inference via Capability Type Structures in Cooperative Multi-Agent Systems. CoRR Charles Jin, Zhang-Wei Hong, Farid Arthaud, Idan Orzech, Martin C. Rinard
2023	A*	`conf`	Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting. ICLR Zhang-Wei Hong, Pulkit Agrawal, Remi Tachet des Combes, Romain Laroche
2023	J	`jnl`	Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting. CoRR Zhang-Wei Hong, Pulkit Agrawal, Rémi Tachet des Combes, Romain Laroche
2023	—	`conf`	Model Predictive Control via On-Policy Imitation Learning. L4DC Kwangjun Ahn, Zakaria Mhammedi, Horia Mania, Zhang-Wei Hong, Ali Jadbabaie
2023	J	`jnl`	Neuro-Inspired Efficient Map Building via Fragmentation and Recall. CoRR Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete
2023	J	`jnl`	Neuro-Inspired Fragmentation and Recall to Overcome Catastrophic Forgetting in Curiosity. CoRR Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete
2023	A*	`conf`	Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation. ICML Zechu Li, Tao Chen, Zhang-Wei Hong, Anurag Ajay, Pulkit Agrawal
2023	J	`jnl`	Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation. CoRR Zechu Li, Tao Chen, Zhang-Wei Hong, Anurag Ajay, Pulkit Agrawal
2023	A*	`conf`	TGRL: An Algorithm for Teacher Guided Reinforcement Learning. ICML Idan Shenfeld, Zhang-Wei Hong, Aviv Tamar, Pulkit Agrawal
2023	J	`jnl`	TGRL: An Algorithm for Teacher Guided Reinforcement Learning. CoRR Idan Shenfeld, Zhang-Wei Hong, Aviv Tamar, Pulkit Agrawal
2022	A*	`conf`	Bi-linear Value Networks for Multi-goal Reinforcement Learning. ICLR Zhang-Wei Hong, Ge Yang, Pulkit Agrawal
2022	J	`jnl`	Bilinear value networks. CoRR Zhang-Wei Hong, Ge Yang, Pulkit Agrawal
2022	J	`jnl`	Model Predictive Control via On-Policy Imitation Learning. CoRR Kwangjun Ahn, Zakaria Mhammedi, Horia Mania, Zhang-Wei Hong, Ali Jadbabaie
2022	J	`jnl`	Redeeming Intrinsic Rewards via Constrained Optimization. CoRR Eric Chen, Zhang-Wei Hong, Joni Pajarinen, Pulkit Agrawal
2022	A*	`conf`	Redeeming intrinsic rewards via constrained optimization. NeurIPS Eric Chen, Zhang-Wei Hong, Joni Pajarinen, Pulkit Agrawal
2022	A	`conf`	Stubborn: A Strong Baseline for Indoor Object Navigation. IROS Haokuan Luo, Albert Yue, Zhang-Wei Hong, Pulkit Agrawal
2022	J	`jnl`	Stubborn: A Strong Baseline for Indoor Object Navigation. CoRR Haokuan Luo, Albert Yue, Zhang-Wei Hong, Pulkit Agrawal
2022	A*	`conf`	Topological Experience Replay. ICLR Zhang-Wei Hong, Tao Chen, Yen-Chen Lin, Joni Pajarinen, Pulkit Agrawal
2022	J	`jnl`	Topological Experience Replay. CoRR Zhang-Wei Hong, Tao Chen, Yen-Chen Lin, Joni Pajarinen, Pulkit Agrawal
2021	—	`conf`	Periodic Intra-ensemble Knowledge Distillation for Reinforcement Learning. ECML/PKDD (1) Zhang-Wei Hong, Prabhat Nagarajan, Guilherme Maeda
2021	A*	`conf`	Reducing the Deployment-Time Inference Control Costs of Deep Reinforcement Learning Agents via an Asymmetric Architecture. ICRA Chin-Jui Chang, Yu-Wei Chu, Chao-Hsien Ting, Hao-Kang Liu, Zhang-Wei Hong, Chun-Yi Lee
2021	J	`jnl`	Reducing the Deployment-Time Inference Control Costs of Deep Reinforcement Learning Agents via an Asymmetric Architecture. CoRR Chin-Jui Chang, Yu-Wei Chu, Chao-Hsien Ting, Hao-Kang Liu, Zhang-Wei Hong, Chun-Yi Lee
2020	J	`jnl`	Mixture of Step Returns in Bootstrapped DQN. CoRR Po-Han Chiang, Hsuan-Kung Yang, Zhang-Wei Hong, Chun-Yi Lee
2020	J	`jnl`	Periodic Intra-Ensemble Knowledge Distillation for Reinforcement Learning. CoRR Zhang-Wei Hong, Prabhat Nagarajan, Guilherme Maeda
2019	Misc	`conf`	Adversarial Active Exploration for Inverse Dynamics Model Learning. CoRL Zhang-Wei Hong, Tsu-Jui Fu, Tzu-Yun Shann, Chun-Yi Lee
2019	J	`jnl`	Model-based Lookahead Reinforcement Learning. CoRR Zhang-Wei Hong, Joni Pajarinen, Jan Peters
2018	A	`conf`	A Deep Policy Inference Q-Network for Multi-Agent Systems. AAMAS Zhang-Wei Hong, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, Chun-Yi Lee
2018	J	`jnl`	Adversarial Exploration Strategy for Self-Supervised Imitation Learning. CoRR Zhang-Wei Hong, Tsu-Jui Fu, Tzu-Yun Shann, Yi-Hsiang Chang, Chun-Yi Lee
2018	—	`conf`	Diversity-Driven Exploration Strategy for Deep Reinforcement Learning. ICLR (Workshop) Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Chun-Yi Lee
2018	A*	`conf`	Diversity-Driven Exploration Strategy for Deep Reinforcement Learning. NeurIPS Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Tsu-Jui Fu, Chun-Yi Lee
2018	J	`jnl`	Diversity-Driven Exploration Strategy for Deep Reinforcement Learning. CoRR Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Chun-Yi Lee
2018	A*	`conf`	Virtual-to-Real: Learning to Control in Visual Semantic Segmentation. IJCAI Zhang-Wei Hong, Yu-Ming Chen, Hsuan-Kung Yang, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, Brian Hsi-Lin Ho, Chih-Chieh Tu, Tsu-Ching Hsiao, Hsin-Wei Hsiao, Sih-Pin Lai, Yueh-Chuan Chang, Chun-Yi Lee
2018	J	`jnl`	Virtual-to-Real: Learning to Control in Visual Semantic Segmentation. CoRR Zhang-Wei Hong, Yu-Ming Chen, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, Hsuan-Kung Yang, Brian Hsi-Lin Ho, Chih-Chieh Tu, Yueh-Chuan Chang, Tsu-Ching Hsiao, Hsin-Wei Hsiao, Sih-Pin Lai, Chun-Yi Lee
2017	J	`jnl`	A Deep Policy Inference Q-Network for Multi-Agent Systems. CoRR Zhang-Wei Hong, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, Chun-Yi Lee
2017	—	`conf`	Tactics of Adversarial Attack on Deep Reinforcement Learning Agents. ICLR (Workshop) Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, Min Sun
2017	A*	`conf`	Tactics of Adversarial Attack on Deep Reinforcement Learning Agents. IJCAI Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, Min Sun
2017	J	`jnl`	Tactics of Adversarial Attack on Deep Reinforcement Learning Agents. CoRR Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, Min Sun