arXiv subjects

William Laplante

Publications and source records attributed to William Laplante.

3 recordsLinked to original sources

arXiv2026

Conjugate Generalized Bayesian Inference for Discrete Doubly Intractable Problems

Doubly intractable problems occur when both the likelihood and the posterior are available only in unnormalized form, with computationally intractable normalization constants. Bayesian inference then typically requires direct approximation of the posterior through specialized and typically expensive MCMC methods. In this paper, we provide a computationally efficient alternative in the form of a novel generalized Bayesian posterior that allows for conjugate, closed-form or Gibbs-based MCMC inference within the class of exponential family models for discrete data. We derive theoretical guarantees to characterize the asymptotic behavior of the generalized posterior, supporting its use for inference. The method is evaluated on a range of challenging intractable exponential family models, including the Conway-Maxwell-Poisson graphical model of multivariate count data, autoregressive discrete time series models, and Markov random fields such as the Ising and Potts models. The computational gains are significant; in our experiments, the method is between 10 and 6000 times faster than state-of-the-art Bayesian computational methods.

stat.ME↗

arXiv2026

Conservative neural posterior estimation via distributionally robust training

Simulation-based inference with neural posterior estimation (NPE) often yields overconfident and unreliable posteriors under limited simulation budgets. To address this, we propose DRO-NPE, a distributionally robust approach that replaces the standard NPE objective with a worst-case loss over a Wasserstein ambiguity set. We introduce KL-based metrics for miscoverage and miscalibration, and use these to show that the DRO-NPE objective controls overfitting and reduces posterior overconfidence. Our method is tractable, parallelisable, and readily integrates with standard normalising flows. Across benchmark SBI tasks, DRO-NPE consistently improves coverage and calibration, while narrowing the gap between empirical and population NPE loss, leading to more reliable inference in low-simulation regimes.

stat.ML↗

arXiv2025

Robust and Conjugate Spatio-Temporal Gaussian Processes

State-space formulations allow for Gaussian process (GP) regression with linear-in-time computational cost in spatio-temporal settings, but performance typically suffers in the presence of outliers. In this paper, we adapt and specialise the robust and conjugate GP (RCGP) framework of Altamirano et al. (2024) to the spatio-temporal setting. In doing so, we obtain an outlier-robust spatio-temporal GP with a computational cost comparable to classical spatio-temporal GPs. We also overcome the three main drawbacks of RCGPs: their unreliable performance when the prior mean is chosen poorly, their lack of reliable uncertainty quantification, and the need to carefully select a hyperparameter by hand. We study our method extensively in finance and weather forecasting applications, demonstrating that it provides a reliable approach to spatio-temporal modelling in the presence of outliers.

stat.CO↗