Thomas B. Schön

16 papers · 2014–2025 · 9 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🗺️ Taxonomy Completionist (11) 🧭 Keyword Pioneer 🌍 Conference Polyglot (9) 🏃 Academic Marathon (11) 🐝 Cross-Pollinator (11) 🌈 Renaissance Researcher (7) 🌉 Interdisciplinary Bridge 👑 Triple Crown ⚡ Prolific Year (6) 🚀 Conference Pioneer 💎 Century Club (16) 📈 Trend Setter

Conferences

AISTATS (5) ICML (3) UAI (2) ECCV (1) ICLR (1) JMLR (1) L4DC (1) MICCAI (1) NIPS (1)

Top co-authors

Jens Sjölund (6) Zheng Zhao (4) Fredrik K. Gustafsson (4) Ziwei Luo (3) Dave Zachariah (2) Martin Danelljan (2) Daniel Gedon (2) Antônio H. Ribeiro (2) Lawrence M. Murray (1) Muhammad Osama (1)

Keywords

sequential monte carlo (2) image restoration (1) particle markov chain monte carlo (1) bayesian inference (1) kl divergence (1) image denoising (1) importance sampling (1) state-space model (1) particle filter (1) linear quadratic regulator (1) probabilistic programming (1) score matching (1) image deblurring (1) ancestor sampling (1) convex optimization (1) regret bound (1) variational inference (1) entropy regularization (1) stochastic differential equation (1) offline reinforcement learning (1)

Papers

Safe exploration in reproducing kernel Hilbert spaces AISTATS 2025

Efficient Optimization Algorithms for Linear Adversarial Training AISTATS 2025

Conditioning diffusion models by explicit forward-backward bridging AISTATS 2025

Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning NIPS 2024

On Feynman-Kac training of partial Bayesian neural networks AISTATS 2024

Controlling Vision-Language Models for Multi-Task Image Restoration ICLR 2024

No Double Descent in Principal Component Regression: A High-Dimensional Analysis ICML 2024

Online learning in motion modeling for intra-interventional image sequences MICCAI 2024

Uncertainty Estimation with Recursive Feature Machines UAI 2024

Image Restoration with Mean-Reverting Stochastic Differential Equations ICML 2023

Learning Proposals for Practical Energy-Based Regression AISTATS 2022

Energy-Based Models for Deep Probabilistic Regression ECCV 2020

Optimistic robust linear quadratic dual control L4DC 2020

Probabilistic Programming for Birth-Death Models of Evolution Using an Alive Particle Filter with Delayed Sampling UAI 2019

Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding ICML 2019

Particle Gibbs with Ancestor Sampling JMLR 2014