Tim G. J. Rudner

29 papers · 2019–2025 · 8 conferences · across top CS/AI conferences

Achievements

+12 more ↓

🌍 Conference Polyglot (8) 🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🏃 Academic Marathon (6)

🌈 Renaissance Researcher (8) 🗺️ Taxonomy Completionist (49) 🧭 Keyword Pioneer 👥 Mega-Team (25) 🏆 Grand Slam 👑 Triple Crown 🗃️ Keyword Collector (101) ⚡ Prolific Year (7) 🚀 Conference Pioneer 💎 Century Club (29) 🔥 Unstoppable (7) ❓ The Questioner (3)

Conferences

ICML (10) NIPS (9) AISTATS (4) EMNLP (2) AAAI (1) CLEAR (1) ICLR (1) NAACL (1)

Top co-authors

Yarin Gal (9) Andrew Gordon Wilson (7) Yee Whye Teh (5) Andrew G Wilson (3) Shikai Qiu (3) Yucen Lily Li (3) Ravid Shwartz-Ziv (2) Sanyam Kapoor (2) Arman Cohan (2) Gunshi Gupta (2)

Keywords

bayesian inference (4) variational inference (4) reinforcement learning (3) neural network (3) uncertainty quantification (2) representation learning (2) deep gaussian process (2) vision-language model (2) covariate shift (2) bayesian neural network (2) language model (2) semantic segmentation (1) domain generalization (1) domain adaptation (1) policy optimization (1) continual learning (1) active learning (1) imitation learning (1) confidence calibration (1) probabilistic modeling (1)

Papers

Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds AISTATS 2025 MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs EMNLP 2025 Simple Factuality Probes Detect Hallucinations in Long-Form Natural Language Generation EMNLP 2025 Position: Supervised Classifiers Answer the Wrong Questions for OOD Detection ICML 2025 Can Transformers Learn Full Bayesian Inference in Context? ICML 2025 SCIURus: Shared Circuits for Interpretable Uncertainty Representations in Language Models NAACL 2025 Fine-Tuning with Uncertainty-Aware Priors Makes Vision and Language Foundation Models More Reliable AISTATS 2025 Improving Pre-trained Self-Supervised Embeddings Through Effective Entropy Maximization AISTATS 2025 Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control NIPS 2024 A Study of Bayesian Neural Network Surrogates for Bayesian Optimization ICLR 2024 Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design ICML 2024 Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI ICML 2024 Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors AISTATS 2024 Non-Vacuous Generalization Bounds for Large Language Models ICML 2024 Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? CLEAR 2023 Protein Design with Guided Discrete Diffusion NIPS 2023 Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution NIPS 2023 An Information Theory Perspective on Variance-Invariance-Covariance Regularization NIPS 2023 Should We Learn Most Likely Functions or Parameters? NIPS 2023 Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions ICML 2023 Function-Space Regularization in Neural Networks: A Probabilistic Perspective ICML 2023 Continual Learning via Sequential Function-Space Variational Inference ICML 2022 Tractable Function-Space Variational Inference in Bayesian Neural Networks NIPS 2022 On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations NIPS 2021 Outcome-Driven Reinforcement Learning via Variational Inference NIPS 2021 On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes ICML 2021 Inter-domain Deep Gaussian Processes ICML 2020 VIREL: A Variational Inference Framework for Reinforcement Learning NIPS 2019 Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery AAAI 2019