An Alternative Infinite Mixture Of Gaussian Process Experts. - Edward Meeds; Simon Osindero (NIPS, 2005)

Modeling Dyadic Data with Binary Latent Factors. - Edward Meeds; Zoubin Ghahramani; Radford M. Neal; Sam T. Roweis (NIPS, 2006)

Learning stick-figure models using nonparametric Bayesian priors over trees. - Edward Meeds; David A. Ross; Richard S. Zemel; Sam T. Roweis (CVPR, 2008)

Nonparametric Bayesian Methods for Extracting Structure from Data. - Edward Meeds (2008)

GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation. - Edward Meeds; Max Welling (UAI, 2014)

Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference. - Edward Meeds; Max Welling (NIPS, 2015)

Hamiltonian ABC. - Edward Meeds; Robert Leenders; Max Welling (UAI, 2015)

POPE: post optimization posterior evaluation of likelihood free models. - Edward Meeds; Michael Chiang; Mary Lee; Olivier Cinquin; John S. Lowengrub; Max Welling (BMC Bioinform., 2015)

MLitB: machine learning in the browser. - Edward Meeds; Remco Hendriks; Said al Faraby; Magiel Bruntink; Max Welling (PeerJ Comput. Sci., 2015)

Soft Weight-Sharing for Neural Network Compression. - Karen Ullrich; Edward Meeds; Max Welling (ICLR (Poster), 2017)

Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks. - Anqi Wu; Sebastian Nowozin; Edward Meeds; Richard E. Turner; José Miguel Hernández-Lobato; Alexander L. Gaunt (CoRR, 2018)

Deterministic Variational Inference for Robust Bayesian Neural Networks. - Anqi Wu; Sebastian Nowozin; Edward Meeds; Richard E. Turner; José Miguel Hernández-Lobato; Alexander L. Gaunt (ICLR, 2019)

Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems. - Geoffrey Roeder; Paul K. Grant; Andrew Phillips; Neil Dalchau; Edward Meeds (ICML, 2019)

Capturing actionable dynamics with structured latent ordinary differential equations. - Paidamoyo Chapfuwa; Sherri Rose; Lawrence Carin; Edward Meeds; Ricardo Henao (UAI, 2022)

AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires. - Melanie F. Pradier; Niranjani Prasad; Paidamoyo Chapfuwa; Sahra Ghalebikesabi; Maximilian Ilse; Steven Woodhouse; Rebecca Elyanow; Javier Zazo; Javier González Hernández; Julia Greissl; Edward Meeds (MLHC, 2023)

AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires. - Melanie F. Pradier; Niranjani Prasad; Paidamoyo Chapfuwa; Sahra Ghalebikesabi; Max Ilse; Steven Woodhouse; Rebecca Elyanow; Javier Zazo; Javier González Hernández; Julia Greissl; Edward Meeds (CoRR, 2023)

SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training. - Chao Ma 019; Wenbo Gong 001; Meyer Scetbon; Edward Meeds (CoRR, 2024)

Gradient Multi-Normalization for Stateless and Scalable LLM Training. - Meyer Scetbon; Chao Ma 019; Wenbo Gong 001; Edward Meeds (CoRR, 2025)

Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension. - Wenbo Gong 001; Meyer Scetbon; Chao Ma 019; Edward Meeds (CoRR, 2025)

A Fourier Space Perspective on Diffusion Models. - Fabian Falck; Teodora Pandeva; Kiarash Zahirnia; Rachel Lawrence; Richard E. Turner; Edward Meeds; Javier Zazo; Sushrut Karmalkar (CoRR, 2025)