Revisiting Projection-Free Optimization for Strongly Convex Constraint Sets. - Jarrid Rector-Brooks; Jun-Kun Wang; Barzan Mozafari (AAAI, 2019)

Biological Sequence Design with GFlowNets. - Moksh Jain; Emmanuel Bengio; Alex Hernández-García; Jarrid Rector-Brooks; Bonaventure F. P. Dossou; Chanakya Ajit Ekbote; Jie Fu 001; Tianyu Zhang; Michael Kilgour; Dinghuai Zhang; Lena Simine; Payel Das; et al. (ICML, 2022)

RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds in vitro. - Paul Bertin; Jarrid Rector-Brooks; Deepak Sharma; Thomas Gaudelet; Andrew Anighoro; Torsten Gross; Francisco Martinez-Pena; Eileen L. Tang; Suraj M. S; Cristian Regep; Jeremy B. R. Hayter; Maksym Korablyov; et al. (CoRR, 2022)

Multi-Objective GFlowNets. - Moksh Jain; Sharath Chandra Raparthy; Alex Hernández-García; Jarrid Rector-Brooks; Yoshua Bengio; Santiago Miret; Emmanuel Bengio (ICML, 2023)

Learning GFlowNets From Partial Episodes For Improved Convergence And Stability. - Kanika Madan; Jarrid Rector-Brooks; Maksym Korablyov; Emmanuel Bengio; Moksh Jain; Andrei Cristian Nica; Tom Bosc; Yoshua Bengio; Nikolay Malkin (ICML, 2023)

Conditional Flow Matching: Simulation-Free Dynamic Optimal Transport. - Alexander Tong 001; Nikolay Malkin; Guillaume Huguet; Yanlei Zhang; Jarrid Rector-Brooks; Kilian Fatras; Guy Wolf; Yoshua Bengio (CoRR, 2023)

Thompson sampling for improved exploration in GFlowNets. - Jarrid Rector-Brooks; Kanika Madan; Moksh Jain; Maksym Korablyov; Cheng-Hao Liu; Sarath Chandar; Nikolay Malkin; Yoshua Bengio (CoRR, 2023)

DEUP: Direct Epistemic Uncertainty Prediction. - Salem Lahlou; Moksh Jain; Hadi Nekoei; Victor Butoi; Paul Bertin; Jarrid Rector-Brooks; Maksym Korablyov; Yoshua Bengio (Trans. Mach. Learn. Res., 2023)

SE(3)-Stochastic Flow Matching for Protein Backbone Generation. - Avishek Joey Bose; Tara Akhound-Sadegh; Guillaume Huguet; Kilian Fatras; Jarrid Rector-Brooks; Cheng-Hao Liu; Andrei Cristian Nica; Maksym Korablyov; Michael M. Bronstein; Alexander Tong 001 (ICLR, 2024)

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities. - Tara Akhound-Sadegh; Jarrid Rector-Brooks; Avishek Joey Bose; Sarthak Mittal; Pablo Lemos; Cheng-Hao Liu; Marcin Sendera; Siamak Ravanbakhsh; Gauthier Gidel; Yoshua Bengio; Nikolay Malkin; Alexander Tong 001 (ICML, 2024)

Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Generation. - Guillaume Huguet; James Vuckovic; Kilian Fatras; Eric Thibodeau-Laufer; Pablo Lemos; Riashat Islam; Cheng-Hao Liu; Jarrid Rector-Brooks; Tara Akhound-Sadegh; Michael M. Bronstein; Alexander Tong 001; Avishek Joey Bose (NeurIPS, 2024)

Improved off-policy training of diffusion samplers. - Marcin Sendera; Minsu Kim 004; Sarthak Mittal; Pablo Lemos; Luca Scimeca; Jarrid Rector-Brooks; Alexandre Adam; Yoshua Bengio; Nikolay Malkin (NeurIPS, 2024)

Amortizing intractable inference in diffusion models for vision, language, and control. - Siddarth Venkatraman; Moksh Jain; Luca Scimeca; Minsu Kim 004; Marcin Sendera; Mohsin Hasan; Luke Rowe; Sarthak Mittal; Pablo Lemos; Emmanuel Bengio; Alexandre Adam; Jarrid Rector-Brooks; et al. (NeurIPS, 2024)

On diffusion models for amortized inference: Benchmarking and improving stochastic control and sampling. - Marcin Sendera; Minsu Kim 004; Sarthak Mittal; Pablo Lemos; Luca Scimeca; Jarrid Rector-Brooks; Alexandre Adam; Yoshua Bengio; Nikolay Malkin (CoRR, 2024)

Generative Active Learning for the Search of Small-molecule Protein Binders. - Maksym Korablyov; Cheng-Hao Liu; Moksh Jain; Almer M. van der Sloot; Eric Jolicoeur; Edward Ruediger; Andrei Cristian Nica; Emmanuel Bengio; Kostiantyn Lapchevskyi; Daniel St-Cyr; Doris Alexandra Schuetz; Victor Ion Butoi; et al. (CoRR, 2024)

Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation. - Guillaume Huguet; James Vuckovic; Kilian Fatras; Eric Thibodeau-Laufer; Pablo Lemos; Riashat Islam; Cheng-Hao Liu; Jarrid Rector-Brooks; Tara Akhound-Sadegh; Michael M. Bronstein; Alexander Tong 001; Avishek Joey Bose (CoRR, 2024)

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction. - Jarrid Rector-Brooks; Mohsin Hasan; Zhangzhi Peng; Zachary Quinn; Cheng-Hao Liu; Sarthak Mittal; Nouha Dziri; Michael M. Bronstein; Yoshua Bengio; Pranam Chatterjee; Alexander Tong 001; Avishek Joey Bose (CoRR, 2024)

Improving and generalizing flow-based generative models with minibatch optimal transport. - Alexander Tong 001; Kilian Fatras; Nikolay Malkin; Guillaume Huguet; Yanlei Zhang; Jarrid Rector-Brooks; Guy Wolf; Yoshua Bengio (Trans. Mach. Learn. Res., 2024)

Adaptive teachers for amortized samplers. - Minsu Kim 004; Sanghyeok Choi; Taeyoung Yun; Emmanuel Bengio; Leo Feng; Jarrid Rector-Brooks; Sungsoo Ahn; Jinkyoo Park; Nikolay Malkin; Yoshua Bengio (ICLR, 2025)

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction. - Jarrid Rector-Brooks; Mohsin Hasan; Zhangzhi Peng; Cheng-Hao Liu; Sarthak Mittal; Nouha Dziri; Michael M. Bronstein; Pranam Chatterjee; Alexander Tong 001; Joey Bose (ICLR, 2025)

From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training. - Julius Berner; Lorenz Richter; Marcin Sendera; Jarrid Rector-Brooks; Nikolay Malkin (CoRR, 2025)

Path Planning for Masked Diffusion Model Sampling. - Zhangzhi Peng; Zachary Bezemek; Sawan Patel; Jarrid Rector-Brooks; Sherwood Yao; Alexander Tong 001; Pranam Chatterjee (CoRR, 2025)

Solving Bayesian inverse problems with diffusion priors and off-policy RL. - Luca Scimeca; Siddarth Venkatraman; Moksh Jain; Minsu Kim 004; Marcin Sendera; Mohsin Hasan; Luke Rowe; Sarthak Mittal; Pablo Lemos; Emmanuel Bengio; Alexandre Adam; Jarrid Rector-Brooks; et al. (CoRR, 2025)