Aki Vehtari (Aalto University). Practical pre-asymptotic diagnostic of Monte Carlo estimates in Bayesian inference and machine learning

OxCSML at NeurIPS 2021 Oct 6, 2021 The group is participating in NeurIPS 2021. Please feel free to stop by any of our poster sessions or presentations! We have 25 papers accepted to the main program...

Approximate Bayesian computation with surrogate posteriors

A primer on PAC-Bayesian learning *followed by* News from the PAC-Bayes frontline

(Please register to access the zoom link) Speaker: Veronika Rockova (University of Chicago) Title: METROPOLIS-HASTINGS VIA CLASSIFICATION

Bayesian nonparametric models for interaction networks

Title: Self-tuning networks: Amortizing the hypergradient computation for hyperparameter optimization

[OxCSML Seminar] Murat Erdogdu, Friday 3:30pm, Convergence of Online SGD under Infinite Noise Variance, and Non-convexity

Caroline Uhler (MIT) Causality and Autoencoders in the Light of Drug Repurposing for COVID-19.

Welcome! We are excited to host an Oxford NeurIPS Meetup at the Department of Statistics during 9–13th December 2019. During the course of the week, our meetup will include: Streams of tutorials,...

High-dimensional black-box optimisation remains an important yet notoriously challenging problem. Despite the success of Bayesian optimisation methods on continuous domains, domains that are...

OxCSML seminar. Cynthia Rudin (Duke). Do Simpler Models Exist and How Can We Find Them?

OxCSML Seminar Series Speaker: James Johndrow (Wharton)

Group equivariant neural networks are used as building blocks of group invariant neural networks, which have been shown to improve generalisation performance and data efficiency through principled...

Bayesian Neural Networks are often sought after for their strong and trustworthy predictive power. However, inference in these models is often computationally expensive and can be reduced using...

This repo contains materials for the "Deconditional Dowscaling with Gaussian Processes" NeurIPS 2021 paper - shahineb/deconditional-downscaling

We present a variational method for online state estimation and parameter learning in state-space models (SSMs), a ubiquitous class of latent variable models for sequential data. As per standard...

We tackle the problem of conditioning probabilistic programs on distributions of observable variables. Probabilistic programs are usually conditioned on samp...

Reinforcement learning from large-scale offline datasets provides us with the ability to learn policies without potentially unsafe or impractical exploration. Significant progress has been made in...

Code to accompany the paper 'On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes' - timrudner/snr_issues_in_deep_gps