Nikolay Malkin

According to our database1, Nikolay Malkin authored at least 48 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Improving and generalizing flow-based generative models with minibatch optimal transport.
Trans. Mach. Learn. Res., 2024

Action abstractions for amortized sampling.
CoRR, 2024

Proof Flow: Preliminary Study on Generative Flow Network Language Model Tuning for Formal Reasoning.
CoRR, 2024

Adaptive teachers for amortized samplers.
CoRR, 2024

Can a Bayesian Oracle Prevent Harm from an Agent?
CoRR, 2024

On Generalization for Generative Flow Networks.
CoRR, 2024

Amortizing intractable inference in diffusion models for vision, language, and control.
CoRR, 2024

Learning diverse attacks on large language models for robust red-teaming and safety tuning.
CoRR, 2024

Machine learning and information theory concepts towards an AI Mathematician.
CoRR, 2024

Discrete Probabilistic Inference as Control in Multi-path Environments.
CoRR, 2024

V-STaR: Training Verifiers for Self-Taught Reasoners.
CoRR, 2024

On diffusion models for amortized inference: Benchmarking and improving stochastic control and sampling.
CoRR, 2024

PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation.
CoRR, 2024

Improving Gradient-Guided Nested Sampling for Posterior Inference.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

PhyloGFN: Phylogenetic inference with generative flow networks.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Expected flow networks in stochastic environments and two-player zero-sum games.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Amortizing intractable inference in large language models.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Delta-AI: Local objectives for amortized inference in sparse graphical models.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Simulation-Free Schrödinger Bridges via Score and Flow Matching.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Discrete, compositional, and symbolic representations through attractor dynamics.
CoRR, 2023

Thompson sampling for improved exploration in GFlowNets.
CoRR, 2023

BatchGFN: Generative Flow Networks for Batch Active Learning.
CoRR, 2023

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets.
CoRR, 2023

Conditional Flow Matching: Simulation-Free Dynamic Optimal Transport.
CoRR, 2023

Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Better Training of GFlowNets with Local Credit and Incomplete Trajectories.
Proceedings of the International Conference on Machine Learning, 2023

Learning GFlowNets From Partial Episodes For Improved Convergence And Stability.
Proceedings of the International Conference on Machine Learning, 2023

GFlowOut: Dropout with Generative Flow Networks.
Proceedings of the International Conference on Machine Learning, 2023

A theory of continuous generative flow networks.
Proceedings of the International Conference on Machine Learning, 2023

GFlowNet-EM for Learning Compositional Latent Variable Models.
Proceedings of the International Conference on Machine Learning, 2023

GFlowNets and variational inference.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

ThinkSum: Probabilistic reasoning over sets using large language models.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022
The Outcome of the 2021 IEEE GRSS Data Fusion Contest - Track MSD: Multitemporal Semantic Change Detection.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022

Posterior samples of source galaxies in strong gravitational lenses with score-based priors.
CoRR, 2022

Unifying Generative Models with GFlowNets.
CoRR, 2022

Resolving label uncertainty with implicit posterior models.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Trajectory balance: Improved credit assignment in GFlowNets.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Diffusion Models as Plug-and-Play Priors.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Generative Flow Networks for Discrete Probabilistic Modeling.
Proceedings of the International Conference on Machine Learning, 2022

Coherence boosting: When your pretrained language model is not paying enough attention.
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

2021
Boosting coherence of language models.
CoRR, 2021

High-resolution land cover change from low-resolution labels: Simple baselines for the 2021 IEEE GRSS Data Fusion Contest.
CoRR, 2021

GPT Perdetry Test: Generating new meanings for new words.
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

From Local Algorithms to Global Results: Human-Machine Collaboration for Robust Analysis of Geographically Diverse Imagery.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021

Studying word order through iterative shuffling.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

2020
Mining Self-similarity: Label Super-Resolution with Epitomic Representations.
Proceedings of the Computer Vision - ECCV 2020, 2020


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