| Wasserstein robust reinforcement learning MA Abdullah, H Ren, HB Ammar, V Milenkovic, R Luo, M Zhang, J Wang arXiv preprint arXiv:1907.13196, 2019 | 94 | 2019 |
| 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 | 69 | 2021 |
| 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 | 39 | 2021 |
| Spread Divergences M Zhang, P Hayes, T Bird, R Habib, D Barber International Conference on Machine Learning, 2020 | 28* | 2020 |
| Variational f-divergence minimization M Zhang, T Bird, R Habib, T Xu, D Barber arXiv preprint arXiv:1907.11891, 2019 | 28 | 2019 |
| Incorporating neuro-inspired adaptability for continual learning in artificial intelligence L Wang, X Zhang, Q Li, M Zhang, H Su, J Zhu, Y Zhong Nature Machine Intelligence 5 (12), 1356-1368, 2023 | 27 | 2023 |
| 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 | 12 | 2022 |
| Improving vae-based representation learning M Zhang, TZ Xiao, B Paige, D Barber arXiv preprint arXiv:2205.14539, 2022 | 12 | 2022 |
| Generalization Gap in Amortized Inference M Zhang, P Hayes, D Barber Advances in Neural Information Processing Systems, 2022, 2022 | 10 | 2022 |
| Parallel Neural Local Lossless Compression M Zhang, J Townsend, N Kang, D Barber arXiv preprint arXiv:2201.05213, 2022 | 8 | 2022 |
| Spread flows for manifold modelling M Zhang, Y Sun, C Zhang, S Mcdonagh International Conference on Artificial Intelligence and Statistics, 11435-11456, 2023 | 7* | 2023 |
| Out-of-distribution detection with class ratio estimation M Zhang, A Zhang, TZ Xiao, Y Sun, S McDonagh arXiv preprint arXiv:2206.03955, 2022 | 6* | 2022 |
| Moment matching denoising gibbs sampling M Zhang, A Hawkins-Hooker, B Paige, D Barber Advances in Neural Information Processing Systems 36, 2024 | 2 | 2024 |
| Active Preference Learning for Large Language Models W Muldrew, P Hayes, M Zhang, D Barber arXiv preprint arXiv:2402.08114, 2024 | 2 | 2024 |
| Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-tuning M Zhang, S Lan, P Hayes, D Barber arXiv preprint arXiv:2402.12177, 2024 | 1 | 2024 |
| Diffusive Gibbs Sampling W Chen, M Zhang, B Paige, JM Hernández-Lobato, D Barber arXiv preprint arXiv:2402.03008, 2024 | 1 | 2024 |
| Integrated weak learning P Hayes, M Zhang, R Habib, J Burgess, E Yilmaz, D Barber arXiv preprint arXiv:2206.09496, 2022 | 1 | 2022 |
| Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling HJ Cunningham, G Giannone, M Zhang, MP Deisenroth arXiv preprint arXiv:2408.09453, 2024 | | 2024 |
| Diffusion Model With Optimal Covariance Matching Z Ou, M Zhang, A Zhang, TZ Xiao, Y Li, D Barber arXiv preprint arXiv:2406.10808, 2024 | | 2024 |
| Solipsistic Reinforcement Learning M Zhang, PN Hayes, TZ Xiao, A Zhang, D Barber Self-Supervision for Reinforcement Learning Workshop, ICLR 2021, 2021 | | 2021 |
David BarberDepartment of Computer Science, University College LondonVerified email at ucl.ac.uk
