| Neural additive models: Interpretable machine learning with neural nets R Agarwal, L Melnick, N Frosst, X Zhang, B Lengerich, R Caruana, ... Advances in neural information processing systems 34, 4699-4711, 2021 | 421 | 2021 |
| Online Data Poisoning Attack X Zhang, L Lessard, X Zhu Learning for Dynamics and Control, 2020, 2019 | 136* | 2019 |
| Adaptive reward-poisoning attacks against reinforcement learning X Zhang, Y Ma, A Singla, X Zhu International Conference on Machine Learning, 11225-11234, 2020 | 132 | 2020 |
| Representation Learning for Online and Offline RL in Low-rank MDPs M Uehara, X Zhang, W Sun | 130 | 2021 |
| Policy poisoning in batch reinforcement learning and control Y Ma, X Zhang, W Sun, X Zhu NIPS 2019, 2019 | 113 | 2019 |
| Training set debugging using trusted items X Zhang, X Zhu, SJ Wright AAAI 2018, 2018 | 81 | 2018 |
| Efficient reinforcement learning in block mdps: A model-free representation learning approach X Zhang, Y Song, M Uehara, M Wang, A Agarwal, W Sun International Conference on Machine Learning, 26517-26547, 2022 | 61 | 2022 |
| Corruption-robust offline reinforcement learning X Zhang, Y Chen, X Zhu, W Sun International Conference on Artificial Intelligence and Statistics, 5757-5773, 2022 | 56 | 2022 |
| Task-agnostic exploration in reinforcement learning X Zhang, A Singla NIPS 2020, 2020 | 48 | 2020 |
| Axiomatic Interpretability for Multiclass Additive Models. X Zhang, S Tan, P Koch, U Chajewska, R Caruana KDD 2019, 2019 | 44 | 2019 |
| Decentralized gossip-based stochastic bilevel optimization over communication networks S Yang, X Zhang, M Wang Advances in neural information processing systems 35, 238-252, 2022 | 41 | 2022 |
| An Optimal Control Approach to Sequential Machine Teaching L Lessard, X Zhang, X Zhu AISTATS 2019, 2019 | 39 | 2019 |
| Robust policy gradient against strong data corruption X Zhang, Y Chen, X Zhu, W Sun International Conference on Machine Learning, 12391-12401, 2021 | 36 | 2021 |
| Reward poisoning in reinforcement learning: Attacks against unknown learners in unknown environments A Rakhsha, X Zhang, X Zhu, A Singla arXiv preprint arXiv:2102.08492, 2021 | 36 | 2021 |
| Provable benefits of representational transfer in reinforcement learning A Agarwal, Y Song, W Sun, K Wang, M Wang, X Zhang The Thirty Sixth Annual Conference on Learning Theory, 2114-2187, 2023 | 25 | 2023 |
| Controllable and diverse text generation in e-commerce H Shao, J Wang, H Lin, X Zhang, A Zhang, H Ji, T Abdelzaher Proceedings of the Web Conference 2021, 2392-2401, 2021 | 25 | 2021 |
| Off-policy fitted q-evaluation with differentiable function approximators: Z-estimation and inference theory R Zhang, X Zhang, C Ni, M Wang International Conference on Machine Learning, 26713-26749, 2022 | 18 | 2022 |
| Byzantine-robust online and offline distributed reinforcement learning Y Chen, X Zhang, K Zhang, M Wang, X Zhu International Conference on Artificial Intelligence and Statistics, 3230-3269, 2023 | 16 | 2023 |
| Representation learning for general-sum low-rank markov games C Ni, Y Song, X Zhang, C Jin, M Wang arXiv preprint arXiv:2210.16976, 2022 | 15 | 2022 |
| Teacher Improves Learning by Selecting a Training Subset Y Ma, R Nowak, P Rigollet, X Zhang, X Zhu AISTATS 2018, 2018 | 14 | 2018 |
Xiaojin ZhuProfessor in Computer Science at University of Wisconsin-Madison在 cs.wisc.edu 的电子邮件经过验证
