Yee Whye Teh

L3Ms — Lagrange Large Language Models ICLR 2025 Learning to Contextualize Web Pages for Enhanced Decision Making by LLM Agents ICLR 2025 SymDiff: Equivariant Diffusion via Stochastic Symmetrisation ICLR 2025 Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design ICML 2024 EvIL: Evolution Strategies for Generalisable Imitation Learning ICML 2024 Kalman Filter for Online Classification of Non-Stationary Data ICLR 2024 Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset NeurIPS 2024 Online Adaptation of Language Models with a Memory of Amortized Contexts NeurIPS 2024 Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI ICML 2024 SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning ICLR 2024 The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning NeurIPS 2024 Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts ICML 2024 Deep Stochastic Processes via Functional Markov Transition Operators NeurIPS 2023 Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation ICLR 2023 Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions ICML 2023 Geometric Neural Diffusion Processes NeurIPS 2023 Learning Instance-Specific Augmentations by Capturing Local Invariances ICML 2023 Modality-Agnostic Variational Compression of Implicit Neural Representations ICML 2023 Nevis'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research JMLR 2023 Pre-training via Denoising for Molecular Property Prediction ICLR 2023 Synthetic Experience Replay NeurIPS 2023 Amortized Rejection Sampling in Universal Probabilistic Programming AISTATS 2022 Behavior Priors for Efficient Reinforcement Learning JMLR 2022 Conformal Off-Policy Prediction in Contextual Bandits NeurIPS 2022 Continual Learning via Sequential Function-Space Variational Inference ICML 2022 Generative Models as Distributions of Functions AISTATS 2022 On Incorporating Inductive Biases into VAEs ICLR 2022 Riemannian Score-Based Generative Modelling NeurIPS 2022 Tractable Function-Space Variational Inference in Bayesian Neural Networks NeurIPS 2022 Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes ICML 2021 LieTransformer: Equivariant Self-Attention for Lie Groups ICML 2021 Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings AISTATS 2021 Robust Pruning at Initialization ICLR 2021 A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments AISTATS 2020 Bayesian Deep Ensembles via the Neural Tangent Kernel NeurIPS 2020 Bootstrapping neural processes NeurIPS 2020 Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support ICML 2020 Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise ICML 2020 Functional Regularisation for Continual Learning with Gaussian Processes ICLR 2020 How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19? NeurIPS 2020 MetaFun: Meta-Learning with Iterative Functional Updates ICML 2020 Multiplicative Interactions and Where to Find Them ICLR 2020 Non-exchangeable feature allocation models with sublinear growth of the feature sizes AISTATS 2020 Probabilistic Symmetries and Invariant Neural Networks JMLR 2020 Uncertainty Estimation Using a Single Deep Deterministic Neural Network ICML 2020 A Statistical Approach to Assessing Neural Network Robustness ICLR 2019 Attentive Neural Processes ICLR 2019 Augmented Neural ODEs NeurIPS 2019 Continual Unsupervised Representation Learning NeurIPS 2019 Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders NeurIPS 2019 Disentangling Disentanglement in Variational Autoencoders ICML 2019 Do Deep Generative Models Know What They Don't Know? ICLR 2019 Hybrid Models with Deep and Invertible Features ICML 2019 Information asymmetry in KL-regularized RL ICLR 2019 Neural Probabilistic Motor Primitives for Humanoid Control ICLR 2019 Random Tessellation Forests NeurIPS 2019 Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow UAI 2019 Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks ICML 2019 Stacked Capsule Autoencoders NeurIPS 2019 Variational Bayesian Optimal Experimental Design NeurIPS 2019 An Analysis of Categorical Distributional Reinforcement Learning AISTATS 2018 Causal Inference via Kernel Deviance Measures NeurIPS 2018 Conditional Neural Processes ICML 2018 Faithful Inversion of Generative Models for Effective Amortized Inference NeurIPS 2018 Mix & Match Agent Curricula for Reinforcement Learning ICML 2018 Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data NeurIPS 2018 Progress & Compress: A scalable framework for continual learning ICML 2018 Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes AISTATS 2018 Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects NeurIPS 2018 Stochastic Expectation Maximization with Variance Reduction NeurIPS 2018 Tighter Variational Bounds are Not Necessarily Better ICML 2018 Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server JMLR 2017 Poisson intensity estimation with reproducing kernels AISTATS 2017 Poisson Random Fields for Dynamic Feature Models JMLR 2017 Relativistic Monte Carlo AISTATS 2017 Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics JMLR 2016 Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics JMLR 2016 Gaussian Processes for Survival Analysis NeurIPS 2016 Mondrian Forests for Large-Scale Regression when Uncertainty Matters AISTATS 2016 Scalable Structure Discovery in Regression using Gaussian Processes AutoML 2016 A hybrid sampler for Poisson-Kingman mixture models NeurIPS 2015 Bayesian Nonparametric Crowdsourcing JMLR 2015 Expectation Particle Belief Propagation NeurIPS 2015 Particle Gibbs for Bayesian Additive Regression Trees AISTATS 2015 Asynchronous Anytime Sequential Monte Carlo NeurIPS 2014 Distributed Bayesian Posterior Sampling via Moment Sharing NeurIPS 2014 Mondrian Forests: Efficient Online Random Forests NeurIPS 2014 Bayesian Hierarchical Community Discovery NeurIPS 2013 Dependent Normalized Random Measures ICML 2013 Fast MCMC Sampling for Markov Jump Processes and Extensions JMLR 2013 Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space NeurIPS 2013 Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex NeurIPS 2013 Top-down particle filtering for Bayesian decision trees ICML 2013 (Invited talk) Bayesian Tools for Natural Language Learning CoNLL 2011 Mixed Cumulative Distribution Networks AISTATS 2011 Hierarchical Dirichlet Trees for Information Retrieval NAACL 2009 Improving Word Sense Disambiguation Using Topic Features EMNLP 2007 NUS-ML:Improving Word Sense Disambiguation Using Topic Features SemEval 2007 A Hierarchical Bayesian Language Model Based On Pitman-Yor Processes ACL 2006 A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation AISTATS 2005 Infinite Hierarchical Hidden Markov Models AISTATS 2005 Variational Inference for the Indian Buffet Process AISTATS 2005 Energy-Based Models for Sparse Overcomplete Representations JMLR 2003 Stick-breaking Construction for the Indian Buffet Process AISTATS 1999