Invariant representations without adversarial training D Moyer, S Gao, R Brekelmans, A Galstyan, G Ver Steeg Advances in neural information processing systems 31, 2018 | 267 | 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 | 96 | 2019 |
Action Matching: Learning Stochastic Dynamics from Samples K Neklyudov, R Brekelmans, D Severo, A Makhzani International Conference on Machine Learning 2023, 2023 | 51 | 2023 |
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 | 40 | 2018 |
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 | 30* | 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 | 21 | 2020 |
Probabilistic inference in language models via twisted sequential monte carlo S Zhao, R Brekelmans, A Makhzani, R Grosse ICML 2024 (Best Paper Award), 2024 | 20 | 2024 |
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 | 16 | 2019 |
A computational framework for solving Wasserstein Lagrangian flows K Neklyudov, R Brekelmans, A Tong, L Atanackovic, Q Liu, A Makhzani ICML 2024, 2024 | 15 | 2024 |
Improving mutual information estimation with annealed and energy-based bounds R Brekelmans, S Huang, M Ghassemi, GV Steeg, R Grosse, A Makhzani ICLR 2022 (International Conference on Learning Representations), 2023 | 14 | 2023 |
Model-free risk-sensitive reinforcement learning G Delétang, J Grau-Moya, M Kunesch, T Genewein, R Brekelmans, ... arXiv preprint arXiv:2111.02907, 2021 | 13 | 2021 |
Information-Theoretic Diffusion X Kong, R Brekelmans, GV Steeg International Conference on Learning Representations 2023, 2023 | 11 | 2023 |
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 | 11 | 2022 |
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 |
Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling Y Du, M Plainer, R Brekelmans, C Duan, F Noe, CP Gomes, ... NeurIPS 2024 (Spotlight), 2024 | 8 | 2024 |
Likelihood ratio exponential families R Brekelmans, F Nielsen, A Makhzani, A Galstyan, GV Steeg NeurIPS Workshop on Information Geometry, 2020 | 7 | 2020 |
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 | 7 | 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 | 6 | 2017 |
Variational Representations of Annealing Paths: Bregman Information under monotonic embedding R Brekelmans, F Nielsen Information Geometry, 1-36, 2022 | 4* | 2022 |
On Schrödinger Bridge Matching and Expectation Maximization R Brekelmans, K Neklyudov NeurIPS 2023 Workshop Optimal Transport and Machine Learning, 2023 | 1 | 2023 |