| Demystifying MMD GANs M Bińkowski, DJ Sutherland, M Arbel, A Gretton International Conference on Learning Representations, 2018 | 2376 | 2018 |
| Pot: Python optimal transport R Flamary, N Courty, A Gramfort, MZ Alaya, A Boisbunon, S Chambon, ... Journal of Machine Learning Research 22 (78), 1-8, 2021 | 1395 | 2021 |
| Generative models and model criticism via optimized maximum mean discrepancy DJ Sutherland, HY Tung, H Strathmann, S De, A Ramdas, A Smola, ... International Conference on Learning Representations, 2017 | 280 | 2017 |
| Learning Deep Kernels for Non-Parametric Two-Sample Tests F Liu, W Xu, J Lu, G Zhang, A Gretton, DJ Sutherland arXiv preprint arXiv:2002.09116, 2020 | 272 | 2020 |
| On the error of random Fourier features DJ Sutherland, J Schneider Uncertainty in Artificial Intelligence, 2015 | 263 | 2015 |
| Exphormer: Sparse transformers for graphs H Shirzad, A Velingker, B Venkatachalam, DJ Sutherland, AK Sinop International Conference on Machine Learning, 31613-31632, 2023 | 249 | 2023 |
| Does invariant risk minimization capture invariance? P Kamath, A Tangella, D Sutherland, N Srebro International Conference on Artificial Intelligence and Statistics, 4069-4077, 2021 | 174 | 2021 |
| On gradient regularizers for MMD GANs M Arbel, DJ Sutherland, M Bińkowski, A Gretton Advances in neural information processing systems, 6700-6710, 2018 | 127 | 2018 |
| A machine learning approach for dynamical mass measurements of galaxy clusters M Ntampaka, H Trac, DJ Sutherland, N Battaglia, B Póczos, J Schneider The Astrophysical Journal 803 (2), 50, 2015 | 127 | 2015 |
| Self-supervised learning with kernel dependence maximization Y Li, R Pogodin, DJ Sutherland, A Gretton Advances in Neural Information Processing Systems 34, 15543-15556, 2021 | 110 | 2021 |
| Learning deep kernels for exponential family densities L Wenliang, DJ Sutherland, H Strathmann, A Gretton International Conference on Machine Learning, 6737-6746, 2019 | 101 | 2019 |
| Uniform convergence of interpolators: Gaussian width, norm bounds and benign overfitting F Koehler, L Zhou, DJ Sutherland, N Srebro Advances in Neural Information Processing Systems 34, 20657-20668, 2021 | 98 | 2021 |
| Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Machine Learning M Ntampaka, H Trac, DJ Sutherland, S Fromenteau, B Póczos, ... The Astrophysical Journal 831 (2), 2016 | 91 | 2016 |
| Learning dynamics of llm finetuning Y Ren, DJ Sutherland arXiv preprint arXiv:2407.10490, 2024 | 89 | 2024 |
| The role of machine learning in the next decade of cosmology M Ntampaka, C Avestruz, S Boada, J Caldeira, J Cisewski-Kehe, ... arXiv preprint arXiv:1902.10159, 2019 | 87 | 2019 |
| Nonparametric Kernel Estimators for Image Classification B Póczos, L Xiong, DJ Sutherland, J Schneider Computer Vision and Pattern Recognition, 2989-2996, 2012 | 67 | 2012 |
| Queer in AI: A case study in community-led participatory AI OO Queerinai, A Ovalle, A Subramonian, A Singh, C Voelcker, ... Proceedings of the 2023 ACM Conference on Fairness, Accountability, and …, 2023 | 58 | 2023 |
| Object discovery via contrastive learning for weakly supervised object detection J Seo, W Bae, DJ Sutherland, J Noh, D Kim European conference on computer vision, 312-329, 2022 | 56 | 2022 |
| Active learning and search on low-rank matrices DJ Sutherland, B Póczos, J Schneider Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013 | 51 | 2013 |
| Pre-trained perceptual features improve differentially private image generation F Harder, MJ Asadabadi, DJ Sutherland, M Park arXiv preprint arXiv:2205.12900, 2022 | 47* | 2022 |
Arthur GrettonGatsby Computational Neuroscience Unit and Google DeepmindVerified email at gatsby.ucl.ac.uk
