Barber, David

61 publications

ICLR 2025 Improving Probabilistic Diffusion Models with Optimal Diagonal Covariance Matching Zijing Ou, Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yingzhen Li, David Barber

NeurIPS 2025 Incremental Sequence Classification with Temporal Consistency Lucas Maystre, Gabriel Barello, Tudor Berariu, Aleix Cambray, Rares Dolga, Alvaro Ortega Gonzalez, Andrei Cristian Nica, David Barber

ICLRW 2025 Towards Training One-Step Diffusion Models Without Distillation Mingtian Zhang, Jiajun He, Wenlin Chen, Zijing Ou, José Miguel Hernández-Lobato, Bernhard Schölkopf, David Barber

AISTATS 2025 Training Neural Samplers with Reverse Diffusive KL Divergence Jiajun He, Wenlin Chen, Mingtian Zhang, David Barber, José Miguel Hernández-Lobato

TMLR 2025 Unifying Linear-Time Attention via Latent Probabilistic Modelling Rares Dolga, Lucas Maystre, Marius Cobzarenco, David Barber

ICML 2024 Active Preference Learning for Large Language Models William Muldrew, Peter Hayes, Mingtian Zhang, David Barber

ICML 2024 Diffusive Gibbs Sampling Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber

NeurIPS 2023 Moment Matching Denoising Gibbs Sampling Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber

MIDL 2023 Survival Analysis for Idiopathic Pulmonary Fibrosis Using CT Images and Incomplete Clinical Data Ahmed Shahin, Joseph Jacob, Daniel Alexander, David Barber

NeurIPS 2022 Generalization Gap in Amortized Inference Mingtian Zhang, Peter Hayes, David Barber

NeurIPSW 2022 Towards Healing the Blindness of Score Matching Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, Francois-Xavier Briol

ICML 2021 Addressing Catastrophic Forgetting in Few-Shot Problems Pauching Yap, Hippolyt Ritter, David Barber

ACML 2021 Improving Gaussian Mixture Latent Variable Model Convergence with Optimal Transport Benoit Gaujac, Ilya Feige, David Barber

ECML-PKDD 2021 Learning Disentangled Representations with the Wasserstein Autoencoder Benoit Gaujac, Ilya Feige, David Barber

ICLR 2021 Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks Thomas Bird, Friso Kingma, David Barber

ICLR 2020 HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Models James Townsend, Thomas Bird, Julius Kunze, David Barber

ICML 2020 Spread Divergence Mingtian Zhang, Peter Hayes, Thomas Bird, Raza Habib, David Barber

ICLR 2019 Auxiliary Variational MCMC Raza Habib, David Barber

MLJ 2019 Improving Latent Variable Descriptiveness by Modelling Rather than Ad-Hoc Factors Alex Mansbridge, Roberto Fierimonte, Ilya Feige, David Barber

ICLR 2019 Practical Lossless Compression with Latent Variables Using Bits Back Coding James Townsend, Thomas Bird, David Barber

ICLR 2018 A Scalable Laplace Approximation for Neural Networks Hippolyt Ritter, Aleksandar Botev, David Barber

AAAI 2018 Generating Sentences Using a Dynamic Canvas Harshil Shah, Bowen Zheng, David Barber

NeurIPS 2018 Generative Neural Machine Translation Harshil Shah, David Barber

NeurIPS 2018 Modular Networks: Learning to Decompose Neural Computation Louis Kirsch, Julius Kunze, David Barber

NeurIPS 2018 Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting Hippolyt Ritter, Aleksandar Botev, David Barber

AISTATS 2017 Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification Aleksandar Botev, Bowen Zheng, David Barber

ICML 2017 Practical Gauss-Newton Optimisation for Deep Learning Aleksandar Botev, Hippolyt Ritter, David Barber

NeurIPS 2017 Thinking Fast and Slow with Deep Learning and Tree Search Thomas Anthony, Zheng Tian, David Barber

NeurIPS 2017 Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning Zhen He, Shaobing Gao, Liang Xiao, Daxue Liu, Hangen He, David Barber

JMLR 2016 Approximate Newton Methods for Policy Search in Markov Decision Processes Thomas Furmston, Guy Lever, David Barber

ICML 2014 Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations David Barber, Yali Wang

JMLR 2013 Gaussian Kullback-Leibler Approximate Inference Edward Challis, David Barber

NeurIPS 2012 A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes Thomas Furmston, David Barber

NeurIPS 2012 Affine Independent Variational Inference Edward Challis, David Barber

ICML 2012 Bayesian Conditional Cointegration Chris Bracegirdle, David Barber

AISTATS 2011 Concave Gaussian Variational Approximations for Inference in Large-Scale Bayesian Linear Models Edward Challis, David Barber

UAI 2011 Efficient Inference in Markov Control Problems Thomas Furmston, David Barber

ECML-PKDD 2011 Lagrange Dual Decomposition for Finite Horizon Markov Decision Processes Thomas Furmston, David Barber

AISTATS 2011 Switch-Reset Models : Exact and Approximate Inference Chris Bracegirdle, David Barber

AISTATS 2010 Variational Methods for Reinforcement Learning Thomas Furmston, David Barber

UAI 2008 Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite Matrices David Barber

NeurIPS 2006 A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems David Barber, Bertrand Mesot

JMLR 2006 Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems David Barber

NeurIPS 2006 Unified Inference for Variational Bayesian Linear Gaussian State-Space Models David Barber, Silvia Chiappa

ICML 2005 A Graphical Model for Chord Progressions Embedded in a Psychoacoustic Space Jean-François Paiement, Douglas Eck, Samy Bengio, David Barber

NeurIPS 2005 Kernelized Infomax Clustering David Barber, Felix V. Agakov

NeurIPS 2003 Information Maximization in Noisy Channels : A Variational Approach David Barber, Felix V. Agakov

NeurIPS 2002 Dynamic Bayesian Networks with Deterministic Latent Tables David Barber

NeurIPS 2002 Learning in Spiking Neural Assemblies David Barber

NeurIPS 1999 Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks David Barber, Peter Sollich

JAIR 1999 Variational Cumulant Expansions for Intractable Distributions David Barber, Piërre van de Laar

NeCo 1998 Online Learning from Finite Training Sets and Robustness to Input Bias Peter Sollich, David Barber

NeurIPS 1998 Tractable Variational Structures for Approximating Graphical Models David Barber, Wim Wiegerinck

NeurIPS 1997 Ensemble Learning for Multi-Layer Networks David Barber, Christopher M. Bishop

NeurIPS 1997 On-Line Learning from Finite Training Sets in Nonlinear Networks Peter Sollich, David Barber

NeurIPS 1997 Radial Basis Functions: A Bayesian Treatment David Barber, Bernhard Schottky

NeurIPS 1996 Bayesian Model Comparison by Monte Carlo Chaining David Barber, Christopher M. Bishop

NeCo 1996 Does Extra Knowledge Necessarily Improve Generalization? David Barber, David Saad

NeurIPS 1996 Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo David Barber, Christopher K. I. Williams

NeurIPS 1996 Online Learning from Finite Training Sets: An Analytical Case Study Peter Sollich, David Barber

NeCo 1995 Test Error Fluctuations in Finite Linear Perceptrons David Barber, David Saad, Peter Sollich