Thomas B. Schön
17 papers · 2014–2026 · 9 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (38) 🏃 Academic Marathon · Half 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird
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Triple Crown
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Conference Pioneer
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Unstoppable · Bronze
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Prolific Year · Productive
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Century Club · Bronze
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Renaissance Researcher
(6)
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Keyword Collector
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Research topics
Machine Learning
(12)
Optimization & Theory
(7)
Artificial Intelligence
(6)
Bayesian & Probabilistic · Machine Learning
(5)
Deep Learning
(4)
Mathematics & Optimization
(4)
Core Methods
(4)
Reinforcement Learning
(3)
Bayesian & Probabilistic · Artificial Intelligence
(3)
Core AI
(3)
Models
(3)
Optimization
(3)
Computer Vision
(2)
Applications
(2)
Architectures
(2)
Keywords
image restoration
(2)
stochastic differential equation
(2)
sequential monte carlo
(2)
gaussian process
(2)
convex optimization
(2)
bayesian inference
(2)
domain adaptation
(1)
variational inference
(1)
multi-task learning
(1)
model misspecification
(1)
kl divergence
(1)
ridge regression
(1)
adversarial training
(1)
image denoising
(1)
system identification
(1)
offline reinforcement learning
(1)
reproducing kernel hilbert space
(1)
importance sampling
(1)
feature learning
(1)
robust optimization
(1)
Papers
Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes
L4DC 2026
Conditioning diffusion models by explicit forward-backward bridging
AISTATS 2025
Efficient Optimization Algorithms for Linear Adversarial Training
AISTATS 2025
Safe exploration in reproducing kernel Hilbert spaces
AISTATS 2025
Controlling Vision-Language Models for Multi-Task Image Restoration
ICLR 2024
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning
NeurIPS 2024
No Double Descent in Principal Component Regression: A High-Dimensional Analysis
ICML 2024
On Feynman-Kac training of partial Bayesian neural networks
AISTATS 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
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
ICML 2019
Probabilistic Programming for Birth-Death Models of Evolution Using an Alive Particle Filter with Delayed Sampling
UAI 2019
Particle Gibbs with Ancestor Sampling
JMLR 2014