Jain, Moksh

29 publications

ICLR 2025 Action Abstractions for Amortized Sampling Oussama Boussif, Lena Nehale Ezzine, Joseph D Viviano, Michał Koziarski, Moksh Jain, Nikolay Malkin, Emmanuel Bengio, Rim Assouel, Yoshua Bengio

ICLR 2025 Learning Diverse Attacks on Large Language Models for Robust Red-Teaming and Safety Tuning Seanie Lee, Minsu Kim, Lynn Cherif, David Dobre, Juho Lee, Sung Ju Hwang, Kenji Kawaguchi, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, Moksh Jain

ICLRW 2025 Solving Bayesian Inverse Problems with Diffusion Priors and Off-Policy RL Luca Scimeca, Siddarth Venkatraman, Moksh Jain, Minsu Kim, Marcin Sendera, Mohsin Hasan, Alexandre Adam, Yashar Hezaveh, Laurence Perreault-Levasseur, Yoshua Bengio, Glen Berseth, Nikolay Malkin

NeurIPS 2025 Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training Brian R. Bartoldson, Siddarth Venkatraman, James Diffenderfer, Moksh Jain, Tal Ben-Nun, Seanie Lee, Minsu Kim, Johan Obando-Ceron, Yoshua Bengio, Bhavya Kailkhura

ICLRW 2024 Active Learning to Discover Pairwise Genetic Interactions via Representation Learning Moksh Jain, Alisandra Kaye Denton, Shawn T. Whitfield, Aniket Rajiv Didolkar, Berton Earnshaw, Jason Hartford

NeurIPS 2024 Amortizing Intractable Inference in Diffusion Models for Vision, Language, and Control Siddarth Venkatraman, Moksh Jain, Luca Scimeca, Minsu Kim, Marcin Sendera, Mohsin Hasan, Luke Rowe, Sarthak Mittal, Pablo Lemos, Emmanuel Bengio, Alexandre Adam, Jarrid Rector-Brooks, Yoshua Bengio, Glen Berseth, Nikolay Malkin

ICLR 2024 Amortizing Intractable Inference in Large Language Models Edward J Hu, Moksh Jain, Eric Elmoznino, Younesse Kaddar, Guillaume Lajoie, Yoshua Bengio, Nikolay Malkin

NeurIPSW 2024 Learning Diverse Attacks on Large Language Models for Robust Red-Teaming and Safety Tuning Seanie Lee, Minsu Kim, Lynn Cherif, David Dobre, Juho Lee, Sung Ju Hwang, Kenji Kawaguchi, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, Moksh Jain

TMLR 2024 Multi-Fidelity Active Learning with GFlowNets Alex Hernández-García, Nikita Saxena, Moksh Jain, Cheng-Hao Liu, Yoshua Bengio

ICLR 2024 PhyloGFN: Phylogenetic Inference with Generative Flow Networks Ming Yang Zhou, Zichao Yan, Elliot Layne, Nikolay Malkin, Dinghuai Zhang, Moksh Jain, Mathieu Blanchette, Yoshua Bengio

ICLR 2024 Pre-Training and Fine-Tuning Generative Flow Networks Ling Pan, Moksh Jain, Kanika Madan, Yoshua Bengio

NeurIPSW 2024 Proof Flow: Preliminary Study on Generative Flow Network Language Model Tuning for Formal Reasoning Matthew Ho, Vincent Zhu, Xiaoyin Chen, Moksh Jain, Nikolay Malkin, Edwin Zhang

ICLRW 2024 Towards DNA-Encoded Library Generation with GFlowNets Michał Koziarski, Mohammed Abukalam, Vedant Shah, Louis Vaillancourt, Doris Alexandra Schuetz, Moksh Jain, Almer M. van der Sloot, Mathieu Bourgey, Anne Marinier, Yoshua Bengio

ICMLW 2023 BatchGFN: Generative Flow Networks for Batch Active Learning Shreshth A Malik, Salem Lahlou, Andrew Jesson, Moksh Jain, Nikolay Malkin, Tristan Deleu, Yoshua Bengio, Yarin Gal

TMLR 2023 DEUP: Direct Epistemic Uncertainty Prediction Salem Lahlou, Moksh Jain, Hadi Nekoei, Victor I Butoi, Paul Bertin, Jarrid Rector-Brooks, Maksym Korablyov, Yoshua Bengio

ICML 2023 GFlowNet-EM for Learning Compositional Latent Variable Models Edward J Hu, Nikolay Malkin, Moksh Jain, Katie E Everett, Alexandros Graikos, Yoshua Bengio

ICML 2023 GFlowOut: Dropout with Generative Flow Networks Dianbo Liu, Moksh Jain, Bonaventure F. P. Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal, Nikolay Malkin, Chris Chinenye Emezue, Dinghuai Zhang, Nadhir Hassen, Xu Ji, Kenji Kawaguchi, Yoshua 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

NeurIPSW 2023 Multi-Fidelity Active Learning with GFlowNets Alex Hernández-García, Nikita Saxena, Moksh Jain, Cheng-Hao Liu, Yoshua Bengio

ICML 2023 Multi-Objective GFlowNets Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-Garcı́a, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, Emmanuel Bengio

NeurIPSW 2023 Pre-Training and Fine-Tuning Generative Flow Networks Ling Pan, Moksh Jain, Kanika Madan, Yoshua Bengio

UAI 2023 Stochastic Generative Flow Networks Ling Pan, Dinghuai Zhang, Moksh Jain, Longbo Huang, Yoshua Bengio

ICMLW 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

ICML 2022 Biological Sequence Design with GFlowNets Moksh Jain, Emmanuel Bengio, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio

ICLRW 2022 Evaluating Generalization in GFlowNets for Molecule Design Andrei Cristian Nica, Moksh Jain, Emmanuel Bengio, Cheng-Hao Liu, Maksym Korablyov, Michael M. Bronstein, Yoshua Bengio

NeurIPSW 2022 Multi-Objective GFlowNets Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-García, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, Emmanuel Bengio

NeurIPS 2022 Trajectory Balance: Improved Credit Assignment in GFlowNets Nikolay Malkin, Moksh Jain, Emmanuel Bengio, Chen Sun, Yoshua Bengio

NeurIPS 2021 Flow Network Based Generative Models for Non-Iterative Diverse Candidate Generation Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, Yoshua Bengio

ICML 2020 DROCC: Deep Robust One-Class Classification Sachin Goyal, Aditi Raghunathan, Moksh Jain, Harsha Vardhan Simhadri, Prateek Jain