| Invariant representations without adversarial training D Moyer, S Gao, R Brekelmans, A Galstyan, G Ver Steeg Advances in neural information processing systems 31, 2018 | 237 | 2018 |
| Auto-encoding total correlation explanation S Gao, R Brekelmans, G Ver Steeg, A Galstyan The 22nd international conference on artificial intelligence and statistics …, 2019 | 91 | 2019 |
| P4ML: A phased performance-based pipeline planner for automated machine learning Y Gil, KT Yao, V Ratnakar, D Garijo, G Ver Steeg, P Szekely, ... AutoML Workshop at ICML 24, 2018 | 38 | 2018 |
| Action Matching: Learning Stochastic Dynamics from Samples K Neklyudov, R Brekelmans, D Severo, A Makhzani International Conference on Machine Learning 2023, 2023 | 26* | 2023 |
| q-paths: Generalizing the geometric annealing path using power means V Masrani, R Brekelmans, T Bui, F Nielsen, A Galstyan, G Ver Steeg, ... Uncertainty in Artificial Intelligence, 1938-1947, 2021 | 25* | 2021 |
| All in the exponential family: Bregman duality in thermodynamic variational inference R Brekelmans, V Masrani, F Wood, GV Steeg, A Galstyan International Conference on Machine Learning 2020, 2020 | 18 | 2020 |
| Exact rate-distortion in autoencoders via echo noise R Brekelmans, D Moyer, A Galstyan, G Ver Steeg Advances in neural information processing systems 32, 2019 | 15 | 2019 |
| Model-free risk-sensitive reinforcement learning G Delétang, J Grau-Moya, M Kunesch, T Genewein, R Brekelmans, ... arXiv preprint arXiv:2111.02907, 2021 | 11 | 2021 |
| Discovery and separation of features for invariant representation learning A Jaiswal, R Brekelmans, D Moyer, GV Steeg, W AbdAlmageed, ... arXiv preprint arXiv:1912.00646, 2019 | 10 | 2019 |
| Your Policy Regularizer is Secretly an Adversary R Brekelmans, T Genewein, J Grau-Moya, G Delétang, M Kunesch, ... Transactions on Machine Learning Research, https://openreview.net/forum?id …, 2022 | 9 | 2022 |
| Improving Mutual Information Estimation with Annealed and Energy-based Bounds R Brekelmans, S Huang, M Ghassemi, GV Steeg, R Grosse, A Makhzani International Conference on Learning Representations 2022, 2022 | 9 | 2022 |
| Likelihood ratio exponential families R Brekelmans, F Nielsen, A Makhzani, A Galstyan, GV Steeg NeurIPS Workshop on Information Geometry, 2020 | 7 | 2020 |
| Information-Theoretic Diffusion X Kong, R Brekelmans, GV Steeg International Conference on Learning Representations 2023, 2023 | 6 | 2023 |
| Gaussian process bandit optimization of the thermodynamic variational objective V Nguyen, V Masrani, R Brekelmans, M Osborne, F Wood Advances in Neural Information Processing Systems 33, 5764-5775, 2020 | 5 | 2020 |
| Disentangled representations via synergy minimization G Ver Steeg, R Brekelmans, H Harutyunyan, A Galstyan 2017 55th Annual Allerton Conference on Communication, Control, and …, 2017 | 5 | 2017 |
| A computational framework for solving Wasserstein Lagrangian flows K Neklyudov, R Brekelmans, A Tong, L Atanackovic, Q Liu, A Makhzani arXiv preprint arXiv:2310.10649, 2023 | 3 | 2023 |
| Variational Representations of Annealing Paths: Bregman Information under monotonic embedding R Brekelmans, F Nielsen Information Geometry, 1-36, 2024 | 2 | 2024 |
| On Schrödinger Bridge Matching and Expectation Maximization R Brekelmans, K Neklyudov NeurIPS 2023 Workshop Optimal Transport and Machine Learning, 2023 | | 2023 |
