We design new visual illusions by finding “adversarial examples” for principled models of
human perception—specifically, for probabilistic models, which treat vision as Bayesian …

Latent Dirichlet allocation model (LDA) has been widely used in topic modeling. Recent
works have shown the effectiveness of integrating neural network mechanisms with this …

We develop an algorithm for automatic differentiation of Metropolis-Hastings samplers,
allowing us to differentiate through probabilistic inference, even if the model has discrete …

We develop EigenVI, an eigenvalue-based approach for black-box variational inference
(BBVI). EigenVI constructs its variational approximations from orthogonal function …

Sammanfattning Gradient estimation by Monte Carlo methods, to eg find optimization
directions, is an important component of many problems in statistics and machine learning …

Unsupervised feature selection (UFS) aims to identify a subset of the most informative
features from high-dimensional data without labels. However, most existing UFS methods …

Elliptical slice sampling, when adapted to linearly truncated multivariate normal distributions,
is a rejection-free Markov chain Monte Carlo method. At its core, it requires analytically …

Humans have a wide range of cognitive capacities that make us adept at interpreting our
physical world. Every day, we encounter new environments, yet we can parse those …

By simplifying complex real-world phenomena, probabilistic methods have proven able to
accelerate applications in discovery and design. However, classical theory often evaluates …

L'inférence bayésienne a posteriori est une méthodologie générale qui, une fois la valeur
d'une observation Y donnée, permet de découvrir les valeurs probables prises par une …