Disentangled representations via synergy minimization. - Greg Ver Steeg; Rob Brekelmans; Hrayr Harutyunyan; Aram Galstyan (Allerton, 2017)

Invariant Representations without Adversarial Training. - Daniel Moyer; Shuyang Gao; Rob Brekelmans; Aram Galstyan; Greg Ver Steeg (NeurIPS, 2018)

Evading the Adversary in Invariant Representation. - Daniel Moyer; Shuyang Gao; Rob Brekelmans; Greg Ver Steeg; Aram Galstyan (CoRR, 2018)

Auto-Encoding Total Correlation Explanation. - Shuyang Gao; Rob Brekelmans; Greg Ver Steeg; Aram Galstyan (AISTATS, 2019)

Exact Rate-Distortion in Autoencoders via Echo Noise. - Rob Brekelmans; Daniel Moyer; Aram Galstyan; Greg Ver Steeg (NeurIPS, 2019)

Discovery and Separation of Features for Invariant Representation Learning. - Ayush Jaiswal; Rob Brekelmans; Daniel Moyer; Greg Ver Steeg; Wael AbdAlmageed; Premkumar Natarajan (CoRR, 2019)

All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference. - Rob Brekelmans; Vaden Masrani; Frank Wood; Greg Ver Steeg; Aram Galstyan (ICML, 2020)

Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective. - Vu Nguyen; Vaden Masrani; Rob Brekelmans; Michael A. Osborne; Frank Wood (NeurIPS, 2020)

Gaussian Process Bandit Optimization of theThermodynamic Variational Objective. - Vu Nguyen; Vaden Masrani; Rob Brekelmans; Michael A. Osborne; Frank Wood (CoRR, 2020)

Annealed Importance Sampling with q-Paths. - Rob Brekelmans; Vaden Masrani; Thang Bui; Frank Wood; Aram Galstyan; Greg Ver Steeg; Frank Nielsen (CoRR, 2020)

Likelihood Ratio Exponential Families. - Rob Brekelmans; Frank Nielsen; Alireza Makhzani; Aram Galstyan; Greg Ver Steeg (CoRR, 2020)

q-Paths: Generalizing the geometric annealing path using power means. - Vaden Masrani; Rob Brekelmans; Thang Bui; Frank Nielsen; Aram Galstyan; Greg Ver Steeg; Frank Wood (UAI, 2021)

Model-Free Risk-Sensitive Reinforcement Learning. - Grégoire Delétang; Jordi Grau-Moya; Markus Kunesch; Tim Genewein; Rob Brekelmans; Shane Legg; Pedro A. Ortega (CoRR, 2021)

Improving Mutual Information Estimation with Annealed and Energy-Based Bounds. - Rob Brekelmans; Sicong Huang 001; Marzyeh Ghassemi; Greg Ver Steeg; Roger Baker Grosse; Alireza Makhzani (ICLR, 2022)

Rho-Tau Bregman Information and the Geometry of Annealing Paths. - Rob Brekelmans; Frank Nielsen (CoRR, 2022)

Your Policy Regularizer is Secretly an Adversary. - Rob Brekelmans; Tim Genewein; Jordi Grau-Moya; Grégoire Delétang; Markus Kunesch; Shane Legg; Pedro A. Ortega (Trans. Mach. Learn. Res., 2022)

Information-Theoretic Diffusion. - Xianghao Kong; Rob Brekelmans; Greg Ver Steeg (ICLR, 2023)

Action Matching: Learning Stochastic Dynamics from Samples. - Kirill Neklyudov; Rob Brekelmans; Daniel Severo 001; Alireza Makhzani (ICML, 2023)

A Computational Framework for Solving Wasserstein Lagrangian Flows. - Kirill Neklyudov; Rob Brekelmans; Alexander Tong 001; Lazar Atanackovic; Qiang Liu; Alireza Makhzani (ICML, 2024)

Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo. - Stephen Zhao; Rob Brekelmans; Alireza Makhzani; Roger Baker Grosse (ICML, 2024)

Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling. - Yuanqi Du; Michael Plainer; Rob Brekelmans; Chenru Duan; Frank Noé; Carla P. Gomes; Alán Aspuru-Guzik; Kirill Neklyudov (NeurIPS, 2024)

Generalized Flow Matching for Transition Dynamics Modeling. - Haibo Wang; Yuxuan Qiu; Yanze Wang; Rob Brekelmans; Yuanqi Du (CoRR, 2024)

Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts. - Marta Skreta; Tara Akhound-Sadegh; Viktor Ohanesian; Roberto Bondesan; Alán Aspuru-Guzik; Arnaud Doucet; Rob Brekelmans; Alexander Tong 001; Kirill Neklyudov (CoRR, 2025)

Measurement-aligned Flow for Inverse Problem. - Shaorong Zhang; Rob Brekelmans; Yunshu Wu; Greg Ver Steeg (CoRR, 2025)