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David Barber

35 papers · 2006–2025 · 9 conferences · across top CS/AI conferences

Achievements

πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (13) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (7) 🌍 Conference Polyglot (9) πŸƒ Academic Marathon (19) πŸ—ΊοΈ Taxonomy Completionist (14) 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (3) πŸ”¬ Deep Specialist (10) 🌱 Topic Pioneer πŸ† Keyword Champion (4) πŸ—ƒοΈ Keyword Collector (140) πŸ“ˆ Trend Setter πŸ’Ž Century Club (35) πŸ”₯ Unstoppable (10) πŸš€ Conference Pioneer

Conferences

NIPS (11) ICLR (6) ICML (6) AISTATS (5) JMLR (3) ACML (1) CVPR (1) EMNLP (1) MIDL (1)

Top co-authors

Mingtian Zhang (7) Hippolyt Ritter (4) Aleksandar Botev (4) Thomas Bird (4) Peter Hayes (3) Thomas Furmston (3) Edward Challis (3) Hangen He (2) JosΓ© Miguel HernΓ‘ndez-Lobato (2) Zhen He (2)

Keywords

variational inference (8) neural network (4) approximate inference (4) markov decision process (3) kullback-leibler divergence (3) switching linear dynamical system (2) probabilistic model (2) state space model (2) optimal transport (2) gaussian approximation (2) probabilistic modeling (2) kalman filter (2) bayesian learning (2) latent variable model (2) catastrophic forgetting (2) laplace approximation (2) natural gradient (2) gaussian sum smoother (2) policy search (2) generative model (2)

Papers

From Characters to Tokens: Dynamic Grouping with Hierarchical BPE EMNLP 2025
Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching ICLR 2025
Training Neural Samplers with Reverse Diffusive KL Divergence AISTATS 2025
Diffusive Gibbs Sampling ICML 2024
Active Preference Learning for Large Language Models ICML 2024
Moment Matching Denoising Gibbs Sampling NIPS 2023
Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data MIDL 2022
Generalization Gap in Amortized Inference NIPS 2022
Addressing Catastrophic Forgetting in Few-Shot Problems ICML 2021
Improving Gaussian mixture latent variable model convergence with Optimal Transport ACML 2021
Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks ICLR 2021
HiLLoC: lossless image compression with hierarchical latent variable models ICLR 2020
Spread Divergence ICML 2020
Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers CVPR 2019
Auxiliary Variational MCMC ICLR 2019
Practical lossless compression with latent variables using bits back coding ICLR 2019
Modular Networks: Learning to Decompose Neural Computation NIPS 2018
A Scalable Laplace Approximation for Neural Networks ICLR 2018
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting NIPS 2018
Generative Neural Machine Translation NIPS 2018
Practical Gauss-Newton Optimisation for Deep Learning ICML 2017
Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification AISTATS 2017
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning NIPS 2017
Thinking Fast and Slow with Deep Learning and Tree Search NIPS 2017
Approximate Newton Methods for Policy Search in Markov Decision Processes JMLR 2016
Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations ICML 2014
Gaussian Kullback-Leibler Approximate Inference JMLR 2013
Affine Independent Variational Inference NIPS 2012
A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes NIPS 2012
Switch-Reset Models : Exact and Approximate Inference AISTATS 2011
Concave Gaussian Variational Approximations for Inference in Large-Scale Bayesian Linear Models AISTATS 2011
Variational methods for Reinforcement Learning AISTATS 2010
Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems JMLR 2006
A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems NIPS 2006
Unified Inference for Variational Bayesian Linear Gaussian State-Space Models NIPS 2006