| Wasserstein robust reinforcement learning MA Abdullah, H Ren, HB Ammar, V Milenkovic, R Luo, M Zhang, J Wang arXiv preprint arXiv:1907.13196, 2019 | 53 | 2019 |
| On the out-of-distribution generalization of probabilistic image modelling M Zhang, A Zhang, S McDonagh Advances in Neural Information Processing Systems 34, 3811-3823, 2021 | 20 | 2021 |
| Variational f-divergence minimization M Zhang, T Bird, R Habib, T Xu, D Barber arXiv preprint arXiv:1907.11891, 2019 | 20 | 2019 |
| Spread Divergences M Zhang, P Hayes, T Bird, R Habib, D Barber International Conference on Machine Learning, 2020 | 19* | 2020 |
| AFEC: Active forgetting of negative transfer in continual learning L Wang, M Zhang, Z Jia, Q Li, C Bao, K Ma, J Zhu, Y Zhong Advances in Neural Information Processing Systems 34, 22379-22391, 2021 | 13 | 2021 |
| Parallel Neural Local Lossless Compression M Zhang, J Townsend, N Kang, D Barber arXiv preprint arXiv:2201.05213, 2022 | 6 | 2022 |
| Towards healing the blindness of score matching M Zhang, O Key, P Hayes, D Barber, B Paige, FX Briol arXiv preprint arXiv:2209.07396, 2022 | 3 | 2022 |
| Generalization Gap in Amortized Inference M Zhang, P Hayes, D Barber arXiv preprint arXiv:2205.11640, 2022 | 3 | 2022 |
| Flow based models for manifold data M Zhang, Y Sun, S McDonagh, C Zhang arXiv preprint arXiv:2109.14216, 2021 | 3 | 2021 |
| Out-of-distribution detection with class ratio estimation M Zhang, A Zhang, TZ Xiao, Y Sun, S McDonagh arXiv preprint arXiv:2206.03955, 2022 | 2 | 2022 |
| Improving vae-based representation learning M Zhang, TZ Xiao, B Paige, D Barber arXiv preprint arXiv:2205.14539, 2022 | 2 | 2022 |
| Integrated Weak Learning P Hayes, M Zhang, R Habib, J Burgess, E Yilmaz, D Barber arXiv preprint arXiv:2206.09496, 2022 | | 2022 |
| Solipsistic Reinforcement Learning M Zhang, PN Hayes, TZ Xiao, A Zhang, D Barber Self-Supervision for Reinforcement Learning Workshop, ICLR 2021, 2021 | | 2021 |
