Bernoulli Race Particle Filters. - Sebastian M. Schmon; Arnaud Doucet; George Deligiannidis (AISTATS, 2019)

Implicit Priors for Knowledge Sharing in Bayesian Neural Networks. - Jack K. Fitzsimons; Sebastian M. Schmon; Stephen J. Roberts (CoRR, 2019)

Neural ODEs for Multi-State Survival Analysis. - Stefan Groha; Sebastian M. Schmon; Alexander Gusev (CoRR, 2020)

Rethinking Semi-Supervised Learning in VAEs. - Tom Joy; Sebastian M. Schmon; Philip H. S. Torr; N. Siddharth 001; Tom Rainforth (CoRR, 2020)

Capturing Label Characteristics in VAEs. - Tom Joy; Sebastian M. Schmon; Philip H. S. Torr; Siddharth Narayanaswamy; Tom Rainforth (ICLR, 2021)

Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation. - Joel Dyer; Patrick W. Cannon; Sebastian M. Schmon (AISTATS, 2022)

AnoDDPM: Anomaly Detection with Denoising Diffusion Probabilistic Models using Simplex Noise. - Julian Wyatt; Adam Leach; Sebastian M. Schmon; Chris G. Willcocks (CVPR Workshops, 2022)

Learning Multimodal VAEs through Mutual Supervision. - Tom Joy; Yuge Shi; Philip H. S. Torr; Tom Rainforth; Sebastian M. Schmon; Siddharth Narayanaswamy (ICLR, 2022)

Robust Neural Posterior Estimation and Statistical Model Criticism. - Daniel Ward; Patrick Cannon; Mark Beaumont; Matteo Fasiolo; Sebastian M. Schmon (NeurIPS, 2022)

Black-box Bayesian inference for economic agent-based models. - Joel Dyer; Patrick Cannon; J. Doyne Farmer; Sebastian M. Schmon (CoRR, 2022)

Calibrating Agent-based Models to Microdata with Graph Neural Networks. - Joel Dyer; Patrick Cannon; J. Doyne Farmer; Sebastian M. Schmon (CoRR, 2022)

High Performance Simulation for Scalable Multi-Agent Reinforcement Learning. - Jordan Langham-Lopez; Sebastian M. Schmon; Patrick Cannon (CoRR, 2022)

Investigating the Impact of Model Misspecification in Neural Simulation-based Inference. - Patrick Cannon; Daniel Ward; Sebastian M. Schmon (CoRR, 2022)

Optimal scaling of random walk Metropolis algorithms using Bayesian large-sample asymptotics. - Sebastian M. Schmon; Philippe Gagnon (Stat. Comput., 2022)

Approximate Bayesian Computation with Path Signatures. - Joel Dyer; Patrick Cannon; Sebastian M. Schmon (UAI, 2024)

PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis. - Yan Wu; Esther Wershof; Sebastian M. Schmon; Marcel Nassar; Blazej Osinski; Ridvan Eksi; Kun Zhang; Thore Graepel (CoRR, 2024)

Bayesian Computation Meets Topology. - Julius von Rohrscheidt; Bastian Rieck; Sebastian M. Schmon (Trans. Mach. Learn. Res., 2024)