Sebastian Schmon

Publications

Google Scholar

2026

  • arXiv
    Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design
    Sebastian M. Schmon, Daniella Pretorius, Simon Mathis, Rebecca Bartke-Croughan, Aishaini Puvanendran, James Vuckovic, Henry Kenlay, Mária Vlachynská, Alex Bridgland, Ivan Grishin, Sven Over, David Li, Bridget Li, Jonathan Crabbé, Agrin Hilmkil, Alexander W. R. Nelson, David Yuan, Annette Obika, Simon A. A. Kohl · arXiv preprint arXiv:2603.29727
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  • ICML
    Reality and Practice: A Relational Reading of the Platonic Representation Hypothesis
    Sebastian M. Schmon · ICML 2026 Workshop on the Philosophy of Machine Learning (PhilML)
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  • ICML
    Measuring the Ruler: Reading Benchmark Saturation as Evidence
    Sebastian M. Schmon · ICML 2026 Workshop on the Philosophy of Machine Learning (PhilML)
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2025

  • arXiv
    Drug-like antibodies with low immunogenicity in human panels designed with Latent-X2
    Henry Kenlay, Daniella Pretorius, Jonathan Crabbé, Alex Bridgland, Sebastian M. Schmon, Agrin Hilmkil, James Vuckovic, Simon Mathis, Tomas Matteson, Rebecca Bartke-Croughan, Amir Motmaen, Robin Rombach, Mária Vlachynská, Alexander W. R. Nelson, David Yuan, Annette Obika, Simon A. A. Kohl · arXiv preprint arXiv:2512.20263
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  • arXiv
    Latent-X: An Atom-level Frontier Model for De Novo Protein Binder Design
    Alex Bridgland, Jonathan Crabbé, Henry Kenlay, Daniella Pretorius, Sebastian M. Schmon, Agrin Hilmkil, Rebecca Bartke-Croughan, Robin Rombach, Michael Flashman, Tomas Matteson, Simon Mathis, Alexander W. R. Nelson, David Yuan, Annette Obika, Simon A. A. Kohl · arXiv preprint arXiv:2507.19375
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2024

  • JEDC
    Black-box Bayesian inference for agent-based models
    Joel Dyer, Patrick Cannon, J Doyne Farmer, Sebastian Schmon · Journal of Economic Dynamics and Control
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  • UAI
    Approximate Bayesian Computation with Path Signatures
    Joel Dyer, Patrick Cannon, Sebastian M Schmon · The 40th Conference on Uncertainty in Artificial Intelligence, Spotlight, Best Paper Award
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  • 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 · arXiv preprint arXiv:2408.10609
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2022

  • CVPR
    AnoDDPM: Anomaly detection with denoising diffusion probabilistic models using simplex noise
    Julian Wyatt, Adam Leach, Sebastian Schmon, Chris G Willcocks · Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
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  • ICLR
    Denoising diffusion probabilistic models on SO(3) for rotational alignment
    Adam Leach, Sebastian Schmon, Matteo T Degiacomi, Chris G Willcocks · ICLR 2022 Workshop on Geometrical and Topological Representation Learning
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  • ICLR
    Learning Multimodal VAEs through Mutual Supervision
    Tom Joy, Yuge Shi, Philip Torr, Tom Rainforth, Sebastian Schmon, Siddharth N · International Conference on Learning Representations
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  • Investigating the impact of model misspecification in neural simulation-based inference
    Patrick Cannon, Daniel Ward, Sebastian Schmon · arXiv preprint arXiv:2209.01845
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  • NeurIPS
    Robust neural posterior estimation and statistical model criticism
    Daniel Ward, Patrick Cannon, Mark Beaumont, Matteo Fasiolo, Sebastian Schmon · Advances in Neural Information Processing Systems
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  • Calibrating agent-based models to microdata with graph neural networks
    Joel Dyer, Patrick Cannon, J Doyne Farmer, Sebastian Schmon · arXiv preprint arXiv:2206.07570
  • Stat & Comp
    Optimal scaling of random walk Metropolis algorithms using Bayesian large-sample asymptotics
    Sebastian Schmon, Philippe Gagnon · Statistics and Computing
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  • AISTATS
    Amortised likelihood-free inference for expensive time-series simulators with signatured ratio estimation
    Joel Dyer, Patrick W Cannon, Sebastian Schmon · International Conference on Artificial Intelligence and Statistics
  • Approximate Bayesian computation for panel data with signature maximum mean discrepancies
    Joel Dyer, John Fitzgerald, Bastian Rieck, Sebastian Schmon · NeurIPS 2022 Temporal Graph Learning Workshop

2021

  • ICLR
    Capturing Label Characteristics in VAEs
    Tom Joy, Sebastian Schmon, Philip Torr, Siddharth N, Tom Rainforth · International Conference on Learning Representations
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  • Biometrika
    Large-sample asymptotics of the pseudo-marginal method
    Sebastian Schmon, George Deligiannidis, Arnaud Doucet, Michael K Pitt · Biometrika
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  • Deep Signature Statistics for Likelihood-free Time-series Models
    Joel Dyer, Patrick W Cannon, Sebastian Schmon · ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models
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2020

  • Neural ODEs for multi-state survival analysis
    Stefan Groha, Sebastian Schmon, Alexander Gusev · arXiv preprint arXiv:2006.04893
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  • Generalized posteriors in approximate Bayesian computation
    Sebastian Schmon, Patrick W Cannon, Jeremias Knoblauch · arXiv preprint arXiv:2011.08644
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  • On Monte Carlo methods for intractable latent variable models
    Sebastian Schmon · University of Oxford

2019

  • Bernoulli race particle filters
    Sebastian Schmon, Arnaud Doucet, George Deligiannidis · The 22nd International Conference on Artificial Intelligence and Statistics

2017

  • JRSS-A
    Estimating the density of ethnic minorities and aged people in Berlin: multivariate kernel density estimation applied to sensitive georeferenced administrative data protected via measurement error
    Marcus Groß, Ulrich Rendtel, Timo Schmid, Sebastian Schmon, Nikos Tzavidis · Journal of the Royal Statistical Society Series A: Statistics in Society
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