Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach. - Jonathan H. Huggins; Trevor Campbell; Mikolaj J. Kasprzak; Tamara Broderick (CoRR, 2018)

Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. - Jonathan H. Huggins; Trevor Campbell; Mikolaj J. Kasprzak; Tamara Broderick (AISTATS, 2019)

Practical Posterior Error Bounds from Variational Objectives. - Jonathan H. Huggins; Mikolaj J. Kasprzak; Trevor Campbell; Tamara Broderick (CoRR, 2019)

Validated Variational Inference via Practical Posterior Error Bounds. - Jonathan H. Huggins; Mikolaj J. Kasprzak; Trevor Campbell; Tamara Broderick (AISTATS, 2020)

A Targeted Accuracy Diagnostic for Variational Approximations. - Yu Wang; Mikolaj J. Kasprzak; Jonathan H. Huggins (AISTATS, 2023)

How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences. - Mikolaj J. Kasprzak; Ryan Giordano; Tamara Broderick (J. Mach. Learn. Res., 2025)

Quantitative Error Bounds for Scaling Limits of Stochastic Iterative Algorithms. - Xiaoyu Wang; Mikolaj J. Kasprzak; Jeffrey Negrea; Solesne Bourguin; Jonathan H. Huggins (CoRR, 2025)