*[RL] Reinforcement Learning, [NLP] Natural Language Processing, [DS] Data Science
*[RL] Reinforcement Learning, [NLP] Natural Language Processing, [DS] Data Science
2022
Tsung-Yen Yang, Tingnan Zhang, Linda Luu, Sehoon Ha, Jie Tan, Wenhao Yu. "Safe Reinforcement Learning for Legged Locomotion." The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, paper, demo, Google AI Blog post [RL]
Allen Z. Ren, Bharat Govil, Tsung-Yen Yang, Karthik Narasimhan, Anirudha Majumdar. "Leveraging Language for Accelerated Learning of Tool Manipulation." Conference On Robot Learning (CoRL), 2022, paper [RL][NLP]
Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, Peter J. Ramadge. "Learning Physics Constrained Dynamics Using Autoencoder." Neural Information Processing System (NeurIPS), 2022, paper, [DS]
2021
Tsung-Yen Yang*, Michael Hu*, Yinlam Chow, Peter J. Ramadge, Karthik Narasimhan. "Safe Reinforcement Learning with Natural Language Constraints." Neural Information Processing System (NeurIPS), 2021, [**Spotlight**] (*Equal contribution) (also in NeurIPS 2020 Workshop "Deep Reinforcement Learning") paper, demo, [RL] [NLP]
Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, Peter J. Ramadge. "Accelerating Safe Reinforcement Learning with Constraint-mismatched Policies." International Conference on Machine Learning (ICML), 2021, (also in NeurIPS 2020 Workshop "Challenges of Real World Reinforcement Learning" [**Spotlight Talk**]) paper, [RL]
2020
Tsung-Yen Yang, Andrew S. Lan, Karthik Narasimhan. "Robust and Interpretable Grounding of Spatial References with Relation Networks." Findings of Conference on Empirical Methods in Natural Language Processing (Findings of EMNLP), 2020, paper [NLP]
Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, Peter J. Ramadge. "Projection-Based Constrained Policy Optimization." International Conference on Learning Representations (ICLR), 2020, paper, website, slides [RL]
2019
Tsung-Yen Yang, Ryan S. Baker, Christoph Studer, Neil Heffernan, and Andrew S. Lan. "Active learning for student affect detection." International Conference on Educational Data Mining (EDM), 2019 [DS]
Patrick Hansen, Richard Junior Bustamante,Tsung-Yen Yang, Elizabeth Tenorio, Christopher G. Brinton, Mung Chiang, and Andrew S. Lan. "Predicting the timing and quality of responsesin online discussion forums." IEEE International Conference on Distributed Computing Systems (ICDCS), 2019 [DS]
2018
Tsung-Yen Yang, Christopher Brinton, Prateek Mittal, Mung Chiang, and Andrew Lan. "Learning informative and private representations via generative adversarial networks." IEEE International Conference on Big Data (Big Data), 2018 [DS]
Tsung-Yen Yang, Christopher G. Brinton, and Carlee Joe-Wong. "Predicting learner interactions in social learning networks." IEEE Conference on Computer Communications (INFOCOM), 2018 [DS]
Madhumitha Shridharan, Ashley Willingham, Jonathan Spencer,Tsung-Yen Yang, and Christopher Brinton. "Predictive learning analytics for video-watching behavior in MOOCs." Conference on Information Sciences and Systems (CISS), 2018 [DS]
Andrew S. Lan, Christopher G. Brinton,Tsung-Yen Yang, and Mung Chiang. "Behavior-based latent variable model for learner engagement." International Conference on Educational Data Mining (EDM), 2018 [DS]
2017
Tsung-Yen Yang, Christopher G. Brinton, Carlee Joe-Wong, and Mung Chiang. "Behavior-based grade prediction for MOOCs via time series neural networks." IEEE Journal of Selected Topics in Signal Processing, 2017 [DS]