← Authors

Yee Whye Teh

102 papers · 2003–2025 · 13 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (22) πŸ—ΊοΈ Taxonomy Completionist (31) 🌍 Conference Polyglot (13) 🐝 Cross-Pollinator (14) 🌈 Renaissance Researcher (7) πŸŒ‰ Interdisciplinary Bridge 🏠 Conference Loyalist (32) 🌟 Keyword Trendsetter Combo (8) 🐺 Lone Wolf (3) 🀝 Dynamic Duo (11) πŸ‘‘ Triple Crown πŸ† Keyword Champion (3) πŸ‘₯ Mega-Team (25) πŸ”¬ Deep Specialist (25) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (13) πŸš€ Conference Pioneer ❓ The Questioner (2) ⚑ Prolific Year (9) πŸ’Ž Century Club (102) πŸ—ƒοΈ Keyword Collector (102)

Conferences

NIPS (32) ICML (23) ICLR (16) AISTATS (12) JMLR (10) CONLL (2) ACL (1) AUTOML (1) COLING (1) EMNLP (1) NAACL (1) SEMEVAL (1) UAI (1)

Top co-authors

Razvan Pascanu (11) Tom Rainforth (10) Balaji Lakshminarayanan (9) Hyunjik Kim (8) Arnaud Doucet (7) Leonard Hasenclever (6) Jonathan Schwarz (6) Alexandre Galashov (6) Emile Mathieu (6) Frank Wood (5)

Research topics

Deep Learning (1)

Keywords

markov chain monte carlo (12) variational inference (12) bayesian inference (9) generative model (7) gaussian process (6) continual learning (5) neural network (5) stochastic process (4) amortized inference (4) reproducing kernel hilbert space (4) representation learning (4) bayesian nonparametrics (4) uncertainty quantification (3) poisson process (3) variance reduction (3) transfer learning (3) sequential monte carlo (3) deep learning (3) unsupervised learning (3) probabilistic modeling (3)

Papers

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