Mining Self-similarity: Label Super-Resolution with Epitomic Representations. - Nikolay Malkin; Anthony Ortiz; Nebojsa Jojic (ECCV (26), 2020)

Studying word order through iterative shuffling. - Nikolay Malkin; Sameera Lanka; Pranav Goel 001; Nebojsa Jojic (EMNLP (1), 2021)

From Local Algorithms to Global Results: Human-Machine Collaboration for Robust Analysis of Geographically Diverse Imagery. - Nebojsa Jojic; Nikolay Malkin; Caleb Robinson; Anthony Ortiz (IGARSS, 2021)

GPT Perdetry Test: Generating new meanings for new words. - Nikolay Malkin; Sameera Lanka; Pranav Goel 001; Sudha Rao; Nebojsa Jojic (NAACL-HLT, 2021)

High-resolution land cover change from low-resolution labels: Simple baselines for the 2021 IEEE GRSS Data Fusion Contest. - Nikolay Malkin; Caleb Robinson; Nebojsa Jojic (CoRR, 2021)

Boosting coherence of language models. - Nikolay Malkin; Zhen Wang 041; Nebojsa Jojic (CoRR, 2021)

Coherence boosting: When your pretrained language model is not paying enough attention. - Nikolay Malkin; Zhen Wang 041; Nebojsa Jojic (ACL (1), 2022)

Generative Flow Networks for Discrete Probabilistic Modeling. - Dinghuai Zhang; Nikolay Malkin; Zhen Liu 019; Alexandra Volokhova; Aaron C. Courville; Yoshua Bengio (ICML, 2022)

Diffusion Models as Plug-and-Play Priors. - Alexandros Graikos; Nikolay Malkin; Nebojsa Jojic; Dimitris Samaras (NeurIPS, 2022)

Trajectory balance: Improved credit assignment in GFlowNets. - Nikolay Malkin; Moksh Jain; Emmanuel Bengio; Chen Sun 007; Yoshua Bengio (NeurIPS, 2022)

Resolving label uncertainty with implicit posterior models. - Esther Rolf; Nikolay Malkin; Alexandros Graikos; Ana Jojic; Caleb Robinson; Nebojsa Jojic (UAI, 2022)

Unifying Generative Models with GFlowNets. - Dinghuai Zhang; Ricky T. Q. Chen; Nikolay Malkin; Yoshua Bengio (CoRR, 2022)

Posterior samples of source galaxies in strong gravitational lenses with score-based priors. - Alexandre Adam; Adam Coogan; Nikolay Malkin; Ronan Legin; Laurence Perreault Levasseur; Yashar Hezaveh; Yoshua Bengio (CoRR, 2022)

The Outcome of the 2021 IEEE GRSS Data Fusion Contest - Track MSD: Multitemporal Semantic Change Detection. - Zhuohong Li; Fangxiao Lu; Hongyan Zhang 001; Lilin Tu; Jiayi Li 001; Xin Huang 002; Caleb Robinson; Nikolay Malkin; Nebojsa Jojic; Pedram Ghamisi; Ronny Hänsch; Naoto Yokoya (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022)

ThinkSum: Probabilistic reasoning over sets using large language models. - Batu Ozturkler; Nikolay Malkin; Zhen Wang 041; Nebojsa Jojic (ACL (1), 2023)

GFlowNets and variational inference. - Nikolay Malkin; Salem Lahlou; Tristan Deleu; Xu Ji; Edward J. Hu; Katie Everett; Dinghuai Zhang; Yoshua Bengio (ICLR, 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)

A theory of continuous generative flow networks. - Salem Lahlou; Tristan Deleu; Pablo Lemos; Dinghuai Zhang; Alexandra Volokhova; Alex Hernández-García; Léna Néhale Ezzine; Yoshua Bengio; Nikolay Malkin (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; et al. (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)

Better Training of GFlowNets with Local Credit and Incomplete Trajectories. - Ling Pan; Nikolay Malkin; Dinghuai Zhang; Yoshua Bengio (ICML, 2023)

Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network. - Tristan Deleu; Mizu Nishikawa-Toomey; Jithendaraa Subramanian; Nikolay Malkin; Laurent Charlin; Yoshua Bengio (NeurIPS, 2023)

Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets. - Dinghuai Zhang; Hanjun Dai; Nikolay Malkin; Aaron C. Courville; Yoshua Bengio; Ling Pan (NeurIPS, 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)

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets. - Dinghuai Zhang; Hanjun Dai; Nikolay Malkin; Aaron C. Courville; Yoshua Bengio; Ling Pan (CoRR, 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 (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)

Discrete, compositional, and symbolic representations through attractor dynamics. - Andrew Nam; Eric Elmoznino; Nikolay Malkin; Chen Sun 007; Yoshua Bengio; Guillaume Lajoie (CoRR, 2023)

Simulation-Free Schrödinger Bridges via Score and Flow Matching. - Alexander Tong 001; Nikolay Malkin; Kilian Fatras; Lazar Atanackovic; Yanlei Zhang; Guillaume Huguet; Guy Wolf; Yoshua Bengio (AISTATS, 2024)

Delta-AI: Local objectives for amortized inference in sparse graphical models. - Jean-Pierre R. Falet; Hae Beom Lee; Nikolay Malkin; Chen Sun 007; Dragos Secrieru; Dinghuai Zhang; Guillaume Lajoie; Yoshua Bengio (ICLR, 2024)

Amortizing intractable inference in large language models. - Edward J. Hu; Moksh Jain; Eric Elmoznino; Younesse Kaddar; Guillaume Lajoie; Yoshua Bengio; Nikolay Malkin (ICLR, 2024)

Expected flow networks in stochastic environments and two-player zero-sum games. - Marco Jiralerspong; Bilun Sun; Danilo Vucetic; Tianyu Zhang; Yoshua Bengio; Gauthier Gidel; Nikolay Malkin (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)

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)

Improving Gradient-Guided Nested Sampling for Posterior Inference. - Pablo Lemos; Nikolay Malkin; Will Handley; Yoshua Bengio; Yashar Hezaveh; Laurence Perreault Levasseur (ICML, 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)

Discrete Probabilistic Inference as Control in Multi-path Environments. - Tristan Deleu; Padideh Nouri; Nikolay Malkin; Doina Precup; Yoshua Bengio (UAI, 2024)

PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation. - Pablo Lemos; Sammy Sharief; Nikolay Malkin; Laurence Perreault Levasseur; Yashar Hezaveh (CoRR, 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)

V-STaR: Training Verifiers for Self-Taught Reasoners. - Arian Hosseini; Xingdi Yuan; Nikolay Malkin; Aaron C. Courville; Alessandro Sordoni; Rishabh Agarwal (CoRR, 2024)

Machine learning and information theory concepts towards an AI Mathematician. - Yoshua Bengio; Nikolay Malkin (CoRR, 2024)

On Generalization for Generative Flow Networks. - Anas Krichel; Nikolay Malkin; Salem Lahlou; Yoshua Bengio (CoRR, 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 (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)

Mixtures of In-Context Learners. - Giwon Hong; Emile van Krieken; Edoardo Maria Ponti; Nikolay Malkin; Pasquale Minervini (ACL (1), 2025)

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)

Action abstractions for amortized sampling. - Oussama Boussif; Léna Néhale Ezzine; Joseph D. Viviano; Michal 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 004; Lynn Cherif; David Dobre; Juho Lee 001; Sung Ju Hwang; Kenji Kawaguchi; Gauthier Gidel; Yoshua Bengio; Nikolay Malkin; Moksh Jain (ICLR, 2025)

PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation. - Pablo Lemos; Sammy Nasser Sharief; Nikolay Malkin; Salma Salhi; Connor Stone; Laurence Perreault Levasseur; Yashar Hezaveh (ICLR, 2025)

Can a Bayesian Oracle Prevent Harm from an Agent? - Yoshua Bengio; Michael K. Cohen; Nikolay Malkin; Matt MacDermott; Damiano Fornasiere; Pietro Greiner; Younesse Kaddar (UAI, 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)

Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models. - Siddarth Venkatraman; Mohsin Hasan; Minsu Kim 004; Luca Scimeca; Marcin Sendera; Yoshua Bengio; Glen Berseth; Nikolay Malkin (CoRR, 2025)

In-Context Parametric Inference: Point or Distribution Estimators? - Sarthak Mittal; Yoshua Bengio; Nikolay Malkin; Guillaume Lajoie (CoRR, 2025)

Learning Decision Trees as Amortized Structure Inference. - Mohammed Mahfoud; Ghait Boukachab; Michal Koziarski; Alex Hernández-García; Stefan Bauer; Yoshua Bengio; Nikolay Malkin (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)

Fast Flow-based Visuomotor Policies via Conditional Optimal Transport Couplings. - Andreas Sochopoulos; Nikolay Malkin; Nikolaos Tsagkas; João Moura 003; Michael Gienger; Sethu Vijayakumar (CoRR, 2025)

On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning. - Magdalena Proszewska; Nikolay Malkin; N. Siddharth 001 (CoRR, 2025)

Adaptive Destruction Processes for Diffusion Samplers. - Timofei Gritsaev; Nikita Morozov; Kirill Tamogashev; Daniil Tiapkin; Sergey Samsonov; Alexey Naumov; Dmitry P. Vetrov; Nikolay Malkin (CoRR, 2025)

Robust Reinforcement Learning for Discrete Compositional Generation via General Soft Operators. - Marco Jiralerspong; Esther Derman; Danilo Vucetic; Nikolay Malkin; Bilun Sun; Tianyu Zhang; Pierre-Luc Bacon; Gauthier Gidel (CoRR, 2025)