Publications

Improved off-policy training of diffusion samplers
Marcin Sendera
Minsu Kim
Sarthak Mittal
Pablo Lemos
Luca Scimeca
Jarrid Rector-Brooks
Alexandre Adam
Yoshua Bengio
Nikolay Malkin
We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function. We ben… (voir plus)chmark several diffusion-structured inference methods, including simulation-based variational approaches and off-policy methods (continuous generative flow networks). Our results shed light on the relative advantages of existing algorithms while bringing into question some claims from past work. We also propose a novel exploration strategy for off-policy methods, based on local search in the target space with the use of a replay buffer, and show that it improves the quality of samples on a variety of target distributions. Our code for the sampling methods and benchmarks studied is made public at https://github.com/GFNOrg/gfn-diffusion as a base for future work on diffusion models for amortized inference.
2024-02-07
ArXiv (prépublication)
arxiv.org
arxiv.org
Improved off-policy training of diffusion samplers
Marcin Sendera
Minsu Kim
Sarthak Mittal
Pablo Lemos
Luca Scimeca
Jarrid Rector-Brooks
Alexandre Adam
Yoshua Bengio
Nikolay Malkin
We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function. We ben… (voir plus)chmark several diffusion-structured inference methods, including simulation-based variational approaches and off-policy methods (continuous generative flow networks). Our results shed light on the relative advantages of existing algorithms while bringing into question some claims from past work. We also propose a novel exploration strategy for off-policy methods, based on local search in the target space with the use of a replay buffer, and show that it improves the quality of samples on a variety of target distributions. Our code for the sampling methods and benchmarks studied is made public at https://github.com/GFNOrg/gfn-diffusion as a base for future work on diffusion models for amortized inference.
2024-02-07
ArXiv (prépublication)
arxiv.org
arxiv.org
Randomized Confidence Bounds for Stochastic Partial Monitoring
Maxime Heuillet
Ola Ahmad
Audrey Durand
2024-02-07
ArXiv (prépublication)
doi.org
arxiv.org
The Impact of Educational Materials on Parental Anxiety and Productivity: A Clinical Trial in Pediatric Appendicitis
Julia Ferreira
Nadia Safa
Fabio Botelho
Robin Petroze
Hussein Wissanji
Dan Poenaru
Pramod Puligandla
Kenneth Shaw
Maeve Trudeau
Sherif Emil
Elena Guadagno
Jean Martin Laberge
2024-02-07
Journal of Pediatric Surgery (publié)
doi.org
AICOM-MP: an AI-based Monkeypox Detector for Resource-Constrained Environments
Tianyi Yang
Tianze Yang
Andrew Liu
Na An
Jie Tang
Shaoshan Liu
Xue (Steve) Liu
2024-02-06
Connection Science (publié)
doi.org
arxiv.org
AICOM-MP: an AI-based monkeypox detector for resource-constrained environments
Tianyi Yang
Tianze Yang
Andrew Liu
Na An
Shaoshan Liu
Xue (Steve) Liu
2024-02-06
Connection science (publié)
doi.org
Effective Protein-Protein Interaction Exploration with PPIretrieval
Chenqing Hua
Connor W. Coley
Guy Wolf
Doina Precup
Shuangjia Zheng
Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, an… (voir plus)d immune defense. As the accuracy of multi-chain protein complex structure prediction improves, the challenge has shifted towards effectively navigating the vast complex universe to identify potential PPIs. Herein, we propose PPIretrieval, the first deep learning-based model for protein-protein interaction exploration, which leverages existing PPI data to effectively search for potential PPIs in an embedding space, capturing rich geometric and chemical information of protein surfaces. When provided with an unseen query protein with its associated binding site, PPIretrieval effectively identifies a potential binding partner along with its corresponding binding site in an embedding space, facilitating the formation of protein-protein complexes.
2024-02-06
ArXiv (prépublication)
doi.org
arxiv.org
Effective Protein-Protein Interaction Exploration with PPIretrieval
Chenqing Hua
Connor Coley
Guy Wolf
Doina Precup
Shuangjia Zheng
2024-02-06
ArXiv (prépublication)
doi.org
arxiv.org
Effective Protein-Protein Interaction Exploration with PPIretrieval
Chenqing Hua
Connor W. Coley
Guy Wolf
Doina Precup
Shuangjia Zheng
Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, an… (voir plus)d immune defense. As the accuracy of multi-chain protein complex structure prediction improves, the challenge has shifted towards effectively navigating the vast complex universe to identify potential PPIs. Herein, we propose PPIretrieval, the first deep learning-based model for protein-protein interaction exploration, which leverages existing PPI data to effectively search for potential PPIs in an embedding space, capturing rich geometric and chemical information of protein surfaces. When provided with an unseen query protein with its associated binding site, PPIretrieval effectively identifies a potential binding partner along with its corresponding binding site in an embedding space, facilitating the formation of protein-protein complexes.
2024-02-06
ArXiv (prépublication)
doi.org
arxiv.org
PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation
Pablo Lemos
Sammy N. Sharief
Nikolay Malkin
Laurence Perreault-Levasseur
Yashar Hezaveh
We propose a comprehensive sample-based method for assessing the quality of generative models. The proposed approach enables the estimation … (voir plus)of the probability that two sets of samples are drawn from the same distribution, providing a statistically rigorous method for assessing the performance of a single generative model or the comparison of multiple competing models trained on the same dataset. This comparison can be conducted by dividing the space into non-overlapping regions and comparing the number of data samples in each region. The method only requires samples from the generative model and the test data. It is capable of functioning directly on high-dimensional data, obviating the need for dimensionality reduction. Significantly, the proposed method does not depend on assumptions regarding the density of the true distribution, and it does not rely on training or fitting any auxiliary models. Instead, it focuses on approximating the integral of the density (probability mass) across various sub-regions within the data space.
2024-02-06
ArXiv (prépublication)
doi.org
arxiv.org
Polynomial Lawvere Logic
Giorgio Bacci
Radu Mardare
Prakash Panangaden
Gordon D. Plotkin
2024-02-05
ArXiv (prépublication)
doi.org
arxiv.org
Toward Human-AI Alignment in Large-Scale Multi-Player Games
Sugandha Sharma
Guy Davidson
Khimya Khetarpal
Anssi Kanervisto
Udit Arora
Katja Hofmann
Ida Momennejad
Achieving human-AI alignment in complex multi-agent games is crucial for creating trustworthy AI agents that enhance gameplay. We propose a … (voir plus)method to evaluate this alignment using an interpretable task-sets framework, focusing on high-level behavioral tasks instead of low-level policies. Our approach has three components. First, we analyze extensive human gameplay data from Xbox's Bleeding Edge (100K+ games), uncovering behavioral patterns in a complex task space. This task space serves as a basis set for a behavior manifold capturing interpretable axes: fight-flight, explore-exploit, and solo-multi-agent. Second, we train an AI agent to play Bleeding Edge using a Generative Pretrained Causal Transformer and measure its behavior. Third, we project human and AI gameplay to the proposed behavior manifold to compare and contrast. This allows us to interpret differences in policy as higher-level behavioral concepts, e.g., we find that while human players exhibit variability in fight-flight and explore-exploit behavior, AI players tend towards uniformity. Furthermore, AI agents predominantly engage in solo play, while humans often engage in cooperative and competitive multi-agent patterns. These stark differences underscore the need for interpretable evaluation, design, and integration of AI in human-aligned applications. Our study advances the alignment discussion in AI and especially generative AI research, offering a measurable framework for interpretable human-agent alignment in multiplayer gaming.
2024-02-05
ArXiv (prépublication)
doi.org
arxiv.org