David Barber

ORCID

Publication Activity (10 Years)

Years Active: 1995-2024
Publications (10 Years): 77

Top Venues

University of Edinburgh, UK (PHD)

Education History

University of Edinburgh, UK

PHD

Publications

Mingtian Zhang, Shawn Lan, Peter Hayes, David Barber
Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning. CoRR (2024)
Rares Dolga, Kai Biegun, Jake Cunningham, David Barber
RotRNN: Modelling Long Sequences with Rotations. CoRR (2024)
Zijing Ou, Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yingzhen Li, David Barber
Diffusion Model With Optimal Covariance Matching. CoRR (2024)
William Muldrew, Peter Hayes, Mingtian Zhang, David Barber
Active Preference Learning for Large Language Models. CoRR (2024)
Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber
Diffusive Gibbs Sampling. CoRR (2024)
Rares Dolga, Marius Cobzarenco, David Barber
Latent Attention for Linear Time Transformers. CoRR (2024)
Ahmed H. Shahin, An Zhao, Alexander C. Whitehead, Daniel C. Alexander, Joseph Jacob, David Barber
CenTime: Event-conditional modelling of censoring in survival analysis. Medical Image Anal. 91 (2024)
Ahmed H. Shahin, An Zhao, Alexander C. Whitehead, Daniel C. Alexander, Joseph Jacob, David Barber
CenTime: Event-Conditional Modelling of Censoring in Survival Analysis. CoRR (2023)
Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber
Moment Matching Denoising Gibbs Sampling. CoRR (2023)
David Barber
Smoothed Q-learning. CoRR (2023)
Harshil Shah, Arthur Wilcke, Marius Cobzarenco, Cristi Cobzarenco, Edward Challis, David Barber
Generalized Multiple Intent Conditioned Slot Filling. CoRR (2023)
Yaozhi Lu, Shahab Aslani, An Zhao, Ahmed H. Shahin, David Barber, Mark Emberton, Daniel C. Alexander, Joseph Jacob
A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study. CoRR (2023)
Yuhao Chen, Boqun Shu, Mojtaba Moattari, Farhana H. Zulkernine, John A. Queenan, David Barber
SPaDe: A Synonym-based Pain-level Detection Tool for Osteoarthritis. ICDH (2023)
Alessia Paglialonga, Rebecca Theal, Bruce Knox, Robert Kyba, David Barber, Aziz Guergachi, Karim Keshavjee
Applying Patient Segmentation Using Primary Care Electronic Medical Records to Develop a Virtual Peer-to-Peer Intervention for Patients with Type 2 Diabetes. Future Internet 15 (4) (2023)
Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber
Moment Matching Denoising Gibbs Sampling. NeurIPS (2023)
Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, François-Xavier Briol
Towards Healing the Blindness of Score Matching. CoRR (2022)
Mingtian Zhang, Tim Z. Xiao, Brooks Paige, David Barber
Improving VAE-based Representation Learning. CoRR (2022)
Ahmed H. Shahin, Joseph Jacob, Daniel C. Alexander, David Barber
Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data. CoRR (2022)
An Zhao, Ahmed H. Shahin, Yukun Zhou, Eyjolfur Gudmundsson, Adam Szmul, Nesrin Mogulkoc, Frouke Van Beek, Christopher Brereton, Hendrik W. Van Es, Katarina Pontoppidan, Recep Savas, Timothy Wallis, Omer Unat, Marcel Veltkamp, Mark G. Jones, Coline H. M. Van Moorsel, David Barber, Joseph Jacob, Daniel C. Alexander
Prognostic Imaging Biomarker Discovery in Survival Analysis for Idiopathic Pulmonary Fibrosis. MICCAI (8) (2022)
Ahmed H. Shahin, Joseph Jacob, Daniel C. Alexander, David Barber
Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data. MIDL (2022)
Mingtian Zhang, Peter Hayes, David Barber
Generalization Gap in Amortized Inference. NeurIPS (2022)
Mingtian Zhang, Peter Hayes, David Barber
Generalization Gap in Amortized Inference. CoRR (2022)
Alessia Paglialonga, Rebecca Theal, David Barber, Robert Kyba, Aziz Guergachi, Karim Keshavjee
Behavioral Segmentation for Enhanced Peer-to-Peer Patient Education. MIE (2022)
Mingtian Zhang, James Townsend, Ning Kang, David Barber
Parallel Neural Local Lossless Compression. CoRR (2022)
Peter Hayes, Mingtian Zhang, Raza Habib, Jordan Burgess, Emine Yilmaz, David Barber
Integrated Weak Learning. CoRR (2022)
Julius Kunze, James Townsend, David Barber
Adaptive Optimization with Examplewise Gradients. CoRR (2021)
Thomas Bird, Friso H. Kingma, David Barber
Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks. ICLR (2021)
Benoit Gaujac, Ilya Feige, David Barber
Improving Gaussian mixture latent variable model convergence with Optimal Transport. ACML (2021)
Benoit Gaujac, Ilya Feige, David Barber
Learning Disentangled Representations with the Wasserstein Autoencoder. ECML/PKDD (3) (2021)
Harshil Shah, Tim Xiao, David Barber
Locally-Contextual Nonlinear CRFs for Sequence Labeling. CoRR (2021)
Emine Yilmaz, Peter Hayes, Raza Habib, Jordan Burgess, David Barber
Sample Efficient Model Evaluation. CoRR (2021)
Pau Ching Yap, Hippolyt Ritter, David Barber
Addressing Catastrophic Forgetting in Few-Shot Problems. ICML (2021)
Benoit Gaujac, Ilya Feige, David Barber
Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders. CoRR (2020)
Thomas Bird, Friso H. Kingma, David Barber
Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks. CoRR (2020)
James Townsend, Thomas Bird, Julius Kunze, David Barber
HiLLoC: lossless image compression with hierarchical latent variable models. ICLR (2020)
David Barber
Private Machine Learning via Randomised Response. CoRR (2020)
Alex Mansbridge, Gregory Barbour, Davide Piras, Christopher Frye, Ilya Feige, David Barber
Learning to Noise: Application-Agnostic Data Sharing with Local Differential Privacy. CoRR (2020)
Mingtian Zhang, Peter Hayes, Thomas Bird, Raza Habib, David Barber
Spread Divergence. ICML (2020)
Rachael Morkem, Kenneth Handelman, John A. Queenan, Richard Birtwhistle, David Barber
Validation of an EMR algorithm to measure the prevalence of ADHD in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). BMC Medical Informatics Decis. Mak. 20 (1) (2020)
Benoit Gaujac, Ilya Feige, David Barber
Learning disentangled representations with the Wasserstein Autoencoder. CoRR (2020)
Pau Ching Yap, Hippolyt Ritter, David Barber
Bayesian Online Meta-Learning with Laplace Approximation. CoRR (2020)
James Townsend, Thomas Bird, Julius Kunze, David Barber
HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Models. CoRR (2019)
James Townsend, Thomas Bird, David Barber
Practical lossless compression with latent variables using bits back coding. ICLR (Poster) (2019)
Zhen He, Jian Li, Daxue Liu, Hangen He, David Barber
Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers. CVPR (2019)
Raza Habib, David Barber
Auxiliary Variational MCMC. ICLR (Poster) (2019)
Alex Mansbridge, Roberto Fierimonte, Ilya Feige, David Barber
Improving latent variable descriptiveness by modelling rather than ad-hoc factors. Mach. Learn. 108 (8-9) (2019)
Julius Kunze, Louis Kirsch, Hippolyt Ritter, David Barber
Gaussian Mean Field Regularizes by Limiting Learned Information. CoRR (2019)
Julius Kunze, Louis Kirsch, Hippolyt Ritter, David Barber
Gaussian Mean Field Regularizes by Limiting Learned Information. Entropy 21 (8) (2019)
Mingtian Zhang, Thomas Bird, Raza Habib, Tianlin Xu, David Barber
Variational f-divergence Minimization. CoRR (2019)
James Townsend, Tom Bird, David Barber
Practical Lossless Compression with Latent Variables using Bits Back Coding. CoRR (2019)
Hippolyt Ritter, Aleksandar Botev, David Barber
Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting. CoRR (2018)
Hippolyt Ritter, Aleksandar Botev, David Barber
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting. NeurIPS (2018)
Hippolyt Ritter, Aleksandar Botev, David Barber
A Scalable Laplace Approximation for Neural Networks. ICLR (Poster) (2018)
Harshil Shah, Bowen Zheng, David Barber
Generating Sentences Using a Dynamic Canvas. CoRR (2018)
Michael Judd, Farhana H. Zulkernine, Brent Wolfrom, David Barber, Akshay Rajaram
Detecting Low Back Pain from Clinical Narratives Using Machine Learning Approaches. DEXA Workshops (2018)
Louis Kirsch, Julius Kunze, David Barber
Modular Networks: Learning to Decompose Neural Computation. NeurIPS (2018)
Harshil Shah, David Barber
Generative Neural Machine Translation. NeurIPS (2018)
Harshil Shah, Bowen Zheng, David Barber
Generating Sentences Using a Dynamic Canvas. AAAI (2018)
Benoit Gaujac, Ilya Feige, David Barber
Gaussian mixture models with Wasserstein distance. CoRR (2018)
Zhen He, Jian Li, Daxue Liu, Hangen He, David Barber
Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers. CoRR (2018)
Louis Kirsch, Julius Kunze, David Barber
Modular Networks: Learning to Decompose Neural Computation. CoRR (2018)
David Barber, Mingtian Zhang, Raza Habib, Thomas Bird
Spread Divergences. CoRR (2018)
Thomas Bird, Julius Kunze, David Barber
Stochastic Variational Optimization. CoRR (2018)
Harshil Shah, David Barber
Generative Neural Machine Translation. CoRR (2018)
Alex Mansbridge, Roberto Fierimonte, Ilya Feige, David Barber
Improving latent variable descriptiveness with AutoGen. CoRR (2018)
Harshil Shah, David Barber, Aleksandar Botev
Overdispersed variational autoencoders. IJCNN (2017)
Zhen He, Shaobing Gao, Liang Xiao, Daxue Liu, Hangen He, David Barber
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning. NIPS (2017)
Thomas Anthony, Zheng Tian, David Barber
Thinking Fast and Slow with Deep Learning and Tree Search. CoRR (2017)
Aleksandar Botev, Guy Lever, David Barber
Nesterov's accelerated gradient and momentum as approximations to regularised update descent. IJCNN (2017)
Aleksandar Botev, Hippolyt Ritter, David Barber
Practical Gauss-Newton Optimisation for Deep Learning. ICML (2017)
Thomas Anthony, Zheng Tian, David Barber
Thinking Fast and Slow with Deep Learning and Tree Search. NIPS (2017)
Zhen He, Shaobing Gao, Liang Xiao, Daxue Liu, Hangen He, David Barber
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning. CoRR (2017)
Aleksandar Botev, Bowen Zheng, David Barber
Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification. AISTATS (2017)
Thomas Furmston, Guy Lever, David Barber
Approximate Newton Methods for Policy Search in Markov Decision Processes. J. Mach. Learn. Res. 17 (2016)
Aleksandar Botev, Guy Lever, David Barber
Nesterov's Accelerated Gradient and Momentum as approximations to Regularised Update Descent. CoRR (2016)
Suzanne Biro, Tyler S. Williamson, Jannet Ann Leggett, David Barber, Rachael Morkem, Kieran Moore, Paul Belanger, Brian Mosley, Ian Janssen
Utility of linking primary care electronic medical records with Canadian census data to study the determinants of chronic disease: an example based on socioeconomic status and obesity. BMC Medical Informatics Decis. Mak. 16 (2016)
Daniel Lafrenière, Farhana H. Zulkernine, David Barber, Ken Martin
Using machine learning to predict hypertension from a clinical dataset. SSCI (2016)
Joe Staines, David Barber
Topic factor models: Uncovering thematic structure in equity market data. Intell. Data Anal. 19 (s1) (2015)
David Barber
Keep Taking the Tablets: Integrating the Mobile in Work-Based Learning. BIIML (2014)
David Barber, Yali Wang
Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations. ICML (2014)
Xiaolin Zhang, Wei Wang, Gerald Sze, David Barber, Chris R. Chatwin
An Image Reconstruction Algorithm for 3-D Electrical Impedance Mammography. IEEE Trans. Medical Imaging 33 (12) (2014)
David Barber
On solving Ordinary Differential Equations using Gaussian Processes. CoRR (2014)
Joe Staines, David Barber
Optimization by Variational Bounding. ESANN (2013)
Edward Challis, David Barber
Gaussian Kullback-Leibler approximate inference. J. Mach. Learn. Res. 14 (1) (2013)
David Barber
Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite Matrices CoRR (2012)
Nikos Vlassis, Michael L. Littman, David Barber
On the Computational Complexity of Stochastic Controller Optimization in POMDPs. ACM Trans. Comput. Theory 4 (4) (2012)
Thomas Furmston, David Barber
A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes. NIPS (2012)
Edward Challis, David Barber
Affine Independent Variational Inference. NIPS (2012)
Joe Staines, David Barber
Variational Optimization CoRR (2012)
Chris Bracegirdle, David Barber
Bayesian Conditional Cointegration. ICML (2012)
Thomas Furmston, David Barber
Efficient Inference in Markov Control Problems CoRR (2012)
Thomas Furmston, David Barber
Lagrange Dual Decomposition for Finite Horizon Markov Decision Processes. ECML/PKDD (1) (2011)
Edward Challis, David Barber
Concave Gaussian Variational Approximations for Inference in Large-Scale Bayesian Linear Models. AISTATS (2011)
Thomas Furmston, David Barber
Efficient Inference in Markov Control Problems. UAI (2011)
Nikos Vlassis, Michael L. Littman, David Barber
On the computational complexity of stochastic controller optimization in POMDPs CoRR (2011)
David Barber, Piërre van de Laar
Variational Cumulant Expansions for Intractable Distributions CoRR (2011)
Chris Bracegirdle, David Barber
Switch-Reset Models : Exact and Approximate Inference. AISTATS (2011)
David Barber, A. Taylan Cemgil
Graphical Models for Time-Series. IEEE Signal Process. Mag. 27 (6) (2010)
Thomas Furmston, David Barber
Variational methods for Reinforcement Learning. AISTATS (2010)
Bertrand Mesot, David Barber
A Simple Alternative Derivation of the Expectation Correction Algorithm. IEEE Signal Process. Lett. 16 (2) (2009)

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Publication Activity (10 Years)

Top Topics

Top Venues

David Barber