Jung-Chun Liu, Chi-Hsien Chang, Shao-Hua Sun, Tian-Li Yu, “Integrating Planning and Deep Reinforcement Learning via Automatic Induction of Task Substructures,” International Conference on Learning Representations (ICLR), 2024
Po-Chen Ko, Jiayuan Mao, Yilun Du, Shao-Hua Sun, Joshua B. Tenenbaum, “Learning to Act from Actionless Videos through Dense Correspondences,” International Conference on Learning Representations (ICLR), 2024
Li-Chun Lu*, Shou-Jen Chen*, Tsung-Min Pai, Chan-Hung Yu, Hung-Yi Lee, Shao-Hua Sun, “LLM Discussion: Enhancing the Creativity of Large Language Models via Discussion Framework and Role-Play,” Conference on Language Modeling (COLM), 2024
Liang-Hsuan Tseng*, En-Pei Hu*, Cheng-Han Chiang, Yuan Tseng, Hung-Yi Lee, Lin-shan Lee, Shao-Hua Sun, “REBORN: Reinforcement-Learned Boundary Segmentation with Iterative Training for Unsupervised ASR,” Neural Information Processing Systems (NeurIPS), 2024
Chun-Mao Lai*, Hsiang-Chun Wang*, Ping-Chun Hsieh, Yu-Chiang Frank Wang, Min-Hung Chen, Shao-Hua Sun, “Diffusion-Reward Adversarial Imitation Learning,” Neural Information Processing Systems (NeurIPS), 2024
Bo-Ruei Huang, Chun-Kai Yang, Chun-Mao Lai, Dai-Jie Wu, Shao-Hua Sun, “Diffusion Imitation from Observation,” Neural Information Processing Systems (NeurIPS), 2024
Yu-An Lin*, Chen-Tao Lee*, Chih-Han Yang*, Guan-Ting Liu*, Shao-Hua Sun, “Hierarchical Programmatic Option Framework,” Neural Information Processing Systems (NeurIPS), 2024
Nicholas Collin Suwono, Justin Chih-Yao Chen, Tun Min Hung, Ting-Hao Kenneth Huang, I-Bin Liao, Yung-Hui Li, Lun-Wei Ku, Shao-Hua Sun, “Location-Aware Visual Question Generation with Lightweight Models,” Empirical Methods in Natural Language Processing (EMNLP), 2023
Guan-Ting Liu*, En-Pei Hu*, Pu-Jen Cheng, Hung-Yi Lee, Shao-Hua Sun, “Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs,” International Conference on Machine Learning (ICML) , 2023
Jesse Zhang, Jiahui Zhang, Karl Pertsch, Ziyi Liu, Xiang Ren, Minsuk Chang, Shao-Hua Sun, Joseph J. Lim, “Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance,” Conference on Robot Learning (CoRL), 2023
Shao-Hua Sun*, Hyeonwoo Noh*, Sriram Somasundaram, Joseph J. Lim, “Neural Program Synthesis from Diverse Demonstration Videos,” International Conference on Machine Learning (ICML), 2018
Shao-Hua Sun, Minyoung Huh, Yuan-Hong Liao, Ning Zhang, Joseph J. Lim, “Multi-view to Novel View: Synthesizing Novel Views with Self-Learned Confidence,” European Conference on Computer Vision (ECCV), 2018
Risto Vuorio, Shao-Hua Sun, Hexiang Hu, Joseph J. Lim, “Toward Multimodal Model-Agnostic Meta-Learning,” Neural Information Processing Systems (NeurIPS) Meta-Learning Workshop, 2018
Youngwoon Lee*, Shao-Hua Sun*, Sriram Somasundaram, Edward Hu, Joseph J. Lim, “Composing Complex Skills by Learning Transition Policies,” International Conference on Learning Representations (ICLR), 2019
Minyoung Huh*, Shao-Hua Sun*, Ning Zhang, “Feedback Adversarial Learning: Spatial Feedback for Improving Generative Adversarial Networks,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Risto Vuorio*, Shao-Hua Sun*, Hexiang Hu, Joseph J. Lim, “Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation,” Neural Information Processing Systems (NeurIPS), 2019
Shao-Hua Sun, Te-Lin Wu, Joseph J. Lim, “Program Guided Agent,” International Conference on Learning Representations (ICLR), 2020
Youngwoon Lee, Andrew Szot, Shao-Hua Sun, Joseph J. Lim, “Generalizable Imitation Learning from Observation via Inferring Goal Proximity,” Neural Information Processing Systems (NeurIPS), 2021
Dweep Trivedi*, Jesse Zhang*, Shao-Hua Sun, Joseph J. Lim, “Learning to Synthesize Programs as Interpretable and Generalizable Policies,” Neural Information Processing Systems (NeurIPS), 2021
Taewook Nam, Shao-Hua Sun, Karl Pertsch, Sung Ju Hwang, Joseph J. Lim, “Skill-based Meta-Reinforcement Learning,” International Conference on Learning Representations (ICLR), 2022