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09/21/2023
Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets
Structure learning is the crux of causal inference. Notably, causal disc...
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05/12/2023
Locking and Quacking: Stacking Bayesian model predictions by log-pooling and superposition
Combining predictions from different models is a central problem in Baye...
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03/17/2023
Distill n' Explain: explaining graph neural networks using simple surrogates
Explaining node predictions in graph neural networks (GNNs) often boils ...
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09/29/2022
Provably expressive temporal graph networks
Temporal graph networks (TGNs) have gained prominence as models for embe...
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02/22/2022
Parallel MCMC Without Embarrassing Failures
Embarrassingly parallel Markov Chain Monte Carlo (MCMC) exploits paralle...
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10/22/2020
Rethinking pooling in graph neural networks
Graph pooling is a central component of a myriad of graph neural network...
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04/23/2020
Variance reduction for distributed stochastic gradient MCMC
Stochastic gradient MCMC methods, such as stochastic gradient Langevin d...
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04/09/2019
Meta-analysis of Bayesian analyses
Meta-analysis aims to combine results from multiple related statistical ...
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03/11/2019
