Sebastian M. Schmon

Publication Activity (10 Years)

Years Active: 2019-2022
Publications (10 Years): 17

Top Topics

Bayesian Inference

Reinforcement Learning

Top Venues

Publications

Daniel Ward, Patrick Cannon, Mark Beaumont, Matteo Fasiolo, Sebastian M. Schmon
Robust Neural Posterior Estimation and Statistical Model Criticism. NeurIPS (2022)
Patrick Cannon, Daniel Ward, Sebastian M. Schmon
Investigating the Impact of Model Misspecification in Neural Simulation-based Inference. CoRR (2022)
Joel Dyer, Patrick Cannon, J. Doyne Farmer, Sebastian M. Schmon
Calibrating Agent-based Models to Microdata with Graph Neural Networks. CoRR (2022)
Daniel Ward, Patrick Cannon, Mark Beaumont, Matteo Fasiolo, Sebastian M. Schmon
Robust Neural Posterior Estimation and Statistical Model Criticism. CoRR (2022)
Julian Wyatt, Adam Leach, Sebastian M. Schmon, Chris G. Willcocks
AnoDDPM: Anomaly Detection with Denoising Diffusion Probabilistic Models using Simplex Noise. CVPR Workshops (2022)
Jordan Langham-Lopez, Sebastian M. Schmon, Patrick Cannon
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning. CoRR (2022)
Joel Dyer, Patrick W. Cannon, Sebastian M. Schmon
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation. AISTATS (2022)
Joel Dyer, Patrick Cannon, Sebastian M. Schmon
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation. CoRR (2022)
Joel Dyer, Patrick Cannon, J. Doyne Farmer, Sebastian M. Schmon
Black-box Bayesian inference for economic agent-based models. CoRR (2022)
Tom Joy, Yuge Shi, Philip H. S. Torr, Tom Rainforth, Sebastian M. Schmon, Siddharth Narayanaswamy
Learning Multimodal VAEs through Mutual Supervision. ICLR (2022)
Sebastian M. Schmon, Philippe Gagnon
Optimal scaling of random walk Metropolis algorithms using Bayesian large-sample asymptotics. Stat. Comput. 32 (2) (2022)
Tom Joy, Sebastian M. Schmon, Philip H. S. Torr, Siddharth Narayanaswamy, Tom Rainforth
Capturing Label Characteristics in VAEs. ICLR (2021)
Tom Joy, Yuge Shi, Philip H. S. Torr, Tom Rainforth, Sebastian M. Schmon, N. Siddharth
Learning Multimodal VAEs through Mutual Supervision. CoRR (2021)
Stefan Groha, Sebastian M. Schmon, Alexander Gusev
Neural ODEs for Multi-State Survival Analysis. CoRR (2020)
Tom Joy, Sebastian M. Schmon, Philip H. S. Torr, N. Siddharth, Tom Rainforth
Rethinking Semi-Supervised Learning in VAEs. CoRR (2020)
Jack K. Fitzsimons, Sebastian M. Schmon, Stephen J. Roberts
Implicit Priors for Knowledge Sharing in Bayesian Neural Networks. CoRR (2019)
Sebastian M. Schmon, Arnaud Doucet, George Deligiannidis
Bernoulli Race Particle Filters. AISTATS (2019)