| Combining adaptivity with progression ordering for intelligent tutoring systems T Mu, S Wang, E Andersen, E Brunskill Proceedings of the Fifth Annual ACM Conference on Learning at Scale, 1-4, 2018 | 27 | 2018 |
| Towards Suggesting Actionable Interventions for Wheel-Spinning Students. T Mu, A Jetten, E Brunskill International Educational Data Mining Society, 2020 | 22 | 2020 |
| Deep action conditional neural network for frame prediction in Atari games E Wang, A Kosson, T Mu Technical report, 2017 | 17 | 2017 |
| Automatic adaptive sequencing in a webgame T Mu, S Wang, E Andersen, E Brunskill Intelligent Tutoring Systems: 17th International Conference, ITS 2021 …, 2021 | 8 | 2021 |
| Resource-aware incremental redundancy in feedback and broadcast RD Wesel, K Vakilinia, SVS Ranganathan, D Divsalar, T Mu International Zurich Seminar on Communications-Proceedings, 63-67, 2016 | 8 | 2016 |
| Constraint sampling reinforcement learning: Incorporating expertise for faster learning T Mu, G Theocharous, D Arbour, E Brunskill Proceedings of the AAAI Conference on Artificial Intelligence 36 (7), 7841-7849, 2022 | 7 | 2022 |
| Plots: procedure learning from observations using subtask structure T Mu, K Goel, E Brunskill arXiv preprint arXiv:1904.09162, 2019 | 6 | 2019 |
| Allocating redundancy between erasure coding and channel coding when fading channel diversity grows with codeword length SVS Ranganathan, T Mu, RD Wesel IEEE Transactions on Communications 65 (8), 3226-3237, 2017 | 6 | 2017 |
| Factored DRO: Factored distributionally robust policies for contextual bandits T Mu, Y Chandak, TB Hashimoto, E Brunskill Advances in Neural Information Processing Systems 35, 8318-8331, 2022 | 5 | 2022 |
| Program2Tutor: combining automatic curriculum generation with multi-armed bandits for intelligent tutoring systems T Mu, K Goel, E Brunskill Conference on Neural Information Processing Systems, 2017 | 5 | 2017 |
| Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning T Mu, S Zheng, AR Trott Transactions on Machine Learning Research, 2022 | 2 | 2022 |
| Solving dynamic principal-agent problems with a rationally inattentive principal T Mu, S Zheng, A Trott arXiv preprint arXiv:2202.01691, 2022 | 2 | 2022 |
| Shared autonomy for an interactive AI system S Zhou, T Mu, K Goel, M Bernstein, E Brunskill Adjunct Proceedings of the 31st Annual ACM Symposium on User Interface …, 2018 | 1 | 2018 |
| Optimality and Rate-Compatibility for Erasure-Coded Packet Transmissions when Fading Channel Diversity Increases with Packet Length SVS Ranganathan, T Mu, RD Wesel arXiv preprint arXiv:1602.00761, 2016 | 1 | 2016 |
| Modeling bounded rationality in multi-agent simulations using rationally inattentive reinforcement learning T Mu, S Zheng, AR Trott US Patent App. 17/554,379, 2023 | | 2023 |
| Constraint sampling reinforcement learning for recommendation systems T Mu, G Theocharous, D Arbour US Patent App. 17/174,944, 2022 | | 2022 |
| More Sample Efficient and Robust Reinforcement Learning with Domain Knowledge T Mu Stanford University, 2022 | | 2022 |
| Assessing Dataset Quality using Optimal Experimental Design for Linear Contextual Bandits T Mu, J Lee, E Brunskill | | |
Emma BrunskillAssociate Professor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
