David Barber

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

Achievements

+12 more ↓

🧭 Keyword Pioneer 🌈 Renaissance Researcher (7) 🌉 Interdisciplinary Bridge 🗺️ Taxonomy Completionist (14) 🐣 Hot Topic Early Bird

🏃 Academic Marathon (19) 🗺️ Taxonomy Completionist (14) 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (3) 🔬 Deep Specialist (10) 🌱 Topic Pioneer 🏆 Keyword Champion (4) 🗃️ Keyword Collector (140) 📈 Trend Setter 💎 Century Club (35) 🔥 Unstoppable (10) 🚀 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) José Miguel Hernández-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