Knowledge and generalisation in simple learning systems. - David Barber; David Saad (ESANN, 1995)

Test Error Fluctuations in Finite Linear Perceptrons. - David Barber; David Saad; Peter Sollich (Neural Comput., 1995)

Bayesian Model Comparison by Monte Carlo Chaining. - David Barber; Christopher M. Bishop (NIPS, 1996)

Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo. - David Barber; Christopher K. I. Williams (NIPS, 1996)

Online Learning from Finite Training Sets: An Analytical Case Study. - Peter Sollich; David Barber (NIPS, 1996)

Does Extra Knowledge Necessarily Improve Generalization? - David Barber; David Saad (Neural Comput., 1996)

Finite size effects in neural network algorithms. - David Barber (1996)

Ensemble Learning for Multi-Layer Networks. - David Barber; Christopher M. Bishop (NIPS, 1997)

Radial Basis Functions: A Bayesian Treatment. - David Barber; Bernhard Schottky (NIPS, 1997)

On-line Learning from Finite Training Sets in Nonlinear Networks. - Peter Sollich; David Barber (NIPS, 1997)

OhioLINK: A Consortial Approach to Digital Library Management. - David Barber (D Lib Mag., 1997)

Tractable Variational Structures for Approximating Graphical Models. - David Barber; Wim Wiegerinck (NIPS, 1998)

Online Learning from Finite Training Sets and Robustness to Input Bias. - Peter Sollich; David Barber (Neural Comput., 1998)

Bayesian Classification With Gaussian Processes. - Christopher K. I. Williams; David Barber (IEEE Trans. Pattern Anal. Mach. Intell., 1998)

Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks. - David Barber; Peter Sollich (NIPS, 1999)

Variational Cumulant Expansions for Intractable Distributions. - David Barber; Piërre van de Laar (J. Artif. Intell. Res., 1999)

Deterministic Generative Models for Fast Feature Discovery. - Machiel Westerdijk; David Barber; Wim Wiegerinck (Data Min. Knowl. Discov., 2001)

Learning in Spiking Neural Assemblies. - David Barber (NIPS, 2002)

Dynamic Bayesian Networks with Deterministic Latent Tables. - David Barber (NIPS, 2002)

Approximate Learning in Temporal Hidden Hopfield Models. - Felix V. Agakov; David Barber (ICANN, 2003)

Optimal Hebbian Learning: A Probabilistic Point of View. - Jean-Pascal Pfister; David Barber; Wulfram Gerstner (ICANN, 2003)

Information Maximization in Noisy Channels : A Variational Approach. - David Barber; Felix V. Agakov (NIPS, 2003)

Variational Information Maximization for Neural Coding. - Felix V. Agakov; David Barber (ICONIP, 2004)

An Auxiliary Variational Method. - Felix V. Agakov; David Barber (ICONIP, 2004)

generative independent component analysis for EEG classification. - Silvia Chiappa; David Barber (ESANN, 2005)

A graphical model for chord progressions embedded in a psychoacoustic space. - Jean-François Paiement; Douglas Eck; Samy Bengio; David Barber (ICML, 2005)

Kernelized Infomax Clustering. - Felix V. Agakov; David Barber (NIPS, 2005)

Auxiliary Variational Information Maximization for Dimensionality Reduction. - Felix V. Agakov; David Barber (SLSFS, 2005)

Islands of the Arctic. - David Barber (Cartogr. Int. J. Geogr. Inf. Geovisualization, 2005)

Efficient Kalman Smoothing for Harmonic State-Space Models. - David Barber (ICASSP (3), 2006)

Tagging of name records for genealogical data browsing. - Mike Perrow; David Barber (JCDL, 2006)

Unified Inference for Variational Bayesian Linear Gaussian State-Space Models. - David Barber; Silvia Chiappa (NIPS, 2006)

A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems. - David Barber; Bertrand Mesot (NIPS, 2006)

EEG classification using generative independent component analysis. - Silvia Chiappa; David Barber (Neurocomputing, 2006)

Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems. - David Barber (J. Mach. Learn. Res., 2006)

Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning. - Jean-Pascal Pfister; Taro Toyoizumi; David Barber; Wulfram Gerstner (Neural Comput., 2006)

A generative model for music transcription. - Ali Taylan Cemgil; Hilbert J. Kappen; David Barber (IEEE Trans. Speech Audio Process., 2006)

Stable Belief Propagation in Gaussian Dags. - David Barber; Peter Sollich (ICASSP (2), 2007)

A Bayesian Alternative to Gain Adaptation in Autoregressive Hidden Markov Models. - Bertrand Mesot; David Barber (ICASSP (2), 2007)

Bayesian Factorial Linear Gaussian State-Space Models for Biosignal Decomposition. - Silvia Chiappa; David Barber (IEEE Signal Process. Lett., 2007)

Switching Linear Dynamical Systems for Noise Robust Speech Recognition. - Bertrand Mesot; David Barber (IEEE Trans. Speech Audio Process., 2007)

Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite Matrices. - David Barber (UAI, 2008)

A Simple Alternative Derivation of the Expectation Correction Algorithm. - Bertrand Mesot; David Barber (IEEE Signal Process. Lett., 2009)

Variational methods for Reinforcement Learning. - Thomas Furmston; David Barber (AISTATS, 2010)

Graphical Models for Time-Series. - David Barber; A. Taylan Cemgil (IEEE Signal Process. Mag., 2010)

Lagrange Dual Decomposition for Finite Horizon Markov Decision Processes. - Thomas Furmston; David Barber (ECML/PKDD (1), 2011)

Efficient Inference in Markov Control Problems. - Thomas Furmston; David Barber (UAI, 2011)

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

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

Bayesian reasoning and machine learning. - David Barber (Cambridge University Press, 2012)

Bayesian Conditional Cointegration. - Chris Bracegirdle; David Barber (ICML, 2012)

Affine Independent Variational Inference. - Edward Challis; David Barber (NIPS, 2012)

A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes. - Thomas Furmston; David Barber (NIPS, 2012)

Variational Optimization - Joe Staines; David Barber (CoRR, 2012)

On the Computational Complexity of Stochastic Controller Optimization in POMDPs. - Nikos Vlassis; Michael L. Littman; David Barber (ACM Trans. Comput. Theory, 2012)

Optimization by Variational Bounding. - Joe Staines; David Barber (ESANN, 2013)

Gaussian Kullback-Leibler approximate inference. - Edward Challis; David Barber (J. Mach. Learn. Res., 2013)

Keep Taking the Tablets: Integrating the Mobile in Work-Based Learning. - David Barber (BIIML, 2014)

Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations. - David Barber; Yali Wang (ICML, 2014)

On solving Ordinary Differential Equations using Gaussian Processes. - David Barber (CoRR, 2014)

An Image Reconstruction Algorithm for 3-D Electrical Impedance Mammography. - Xiaolin Zhang; Wei Wang; Gerald Sze; David Barber; Chris R. Chatwin (IEEE Trans. Medical Imaging, 2014)

Topic factor models: Uncovering thematic structure in equity market data. - Joe Staines; David Barber (Intell. Data Anal., 2015)

Using machine learning to predict hypertension from a clinical dataset. - Daniel Lafrenière; Farhana H. Zulkernine; David Barber; Ken Martin (SSCI, 2016)

Approximate Newton Methods for Policy Search in Markov Decision Processes. - Thomas Furmston; Guy Lever; David Barber (J. Mach. Learn. Res., 2016)

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. - Suzanne Biro; Tyler S. Williamson; Jannet Ann Leggett; David Barber; Rachael Morkem; Kieran Moore; Paul Belanger; Brian Mosley; Ian Janssen (BMC Medical Informatics Decis. Mak., 2016)

Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification. - Aleksandar Botev; Bowen Zheng 004; David Barber (AISTATS, 2017)

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

Nesterov's accelerated gradient and momentum as approximations to regularised update descent. - Aleksandar Botev; Guy Lever; David Barber (IJCNN, 2017)

Overdispersed variational autoencoders. - Harshil Shah; David Barber; Aleksandar Botev (IJCNN, 2017)

Thinking Fast and Slow with Deep Learning and Tree Search. - Thomas Anthony 001; Zheng Tian 002; David Barber (NIPS, 2017)

Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning. - Zhen He; Shaobing Gao; Liang Xiao 007; Daxue Liu; Hangen He; David Barber (NIPS, 2017)

Generating Sentences Using a Dynamic Canvas. - Harshil Shah; Bowen Zheng 004; David Barber (AAAI, 2018)

Detecting Low Back Pain from Clinical Narratives Using Machine Learning Approaches. - Michael Judd; Farhana H. Zulkernine; Brent Wolfrom; David Barber; Akshay Rajaram (DEXA Workshops, 2018)

A Scalable Laplace Approximation for Neural Networks. - Hippolyt Ritter; Aleksandar Botev; David Barber (ICLR (Poster), 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 (NeurIPS, 2018)

Generative Neural Machine Translation. - Harshil Shah; David Barber (NeurIPS, 2018)

Gaussian mixture models with Wasserstein distance. - Benoit Gaujac; Ilya Feige; David Barber (CoRR, 2018)

Improving latent variable descriptiveness with AutoGen. - Alex Mansbridge; Roberto Fierimonte; Ilya Feige; David Barber (CoRR, 2018)

Stochastic Variational Optimization. - Thomas Bird; Julius Kunze; David Barber (CoRR, 2018)

Spread Divergences. - David Barber; Mingtian Zhang; Raza Habib; Thomas Bird (CoRR, 2018)

Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers. - Zhen He; Jian Li 003; Daxue Liu; Hangen He; David Barber (CVPR, 2019)

Auxiliary Variational MCMC. - Raza Habib; David Barber (ICLR (Poster), 2019)

Practical lossless compression with latent variables using bits back coding. - James Townsend; Thomas Bird; David Barber (ICLR (Poster), 2019)

Practical Lossless Compression with Latent Variables using Bits Back Coding. - James Townsend; Tom Bird; David Barber (CoRR, 2019)

Variational f-divergence Minimization. - Mingtian Zhang; Thomas Bird; Raza Habib; Tianlin Xu; David Barber (CoRR, 2019)

Gaussian Mean Field Regularizes by Limiting Learned Information. - Julius Kunze; Louis Kirsch; Hippolyt Ritter; David Barber (Entropy, 2019)

Improving latent variable descriptiveness by modelling rather than ad-hoc factors. - Alex Mansbridge; Roberto Fierimonte; Ilya Feige; David Barber (Mach. Learn., 2019)

HiLLoC: lossless image compression with hierarchical latent variable models. - James Townsend; Thomas Bird; Julius Kunze; David Barber (ICLR, 2020)

Spread Divergence. - Mingtian Zhang; Peter Hayes; Thomas Bird; Raza Habib; David Barber (ICML, 2020)

Private Machine Learning via Randomised Response. - David Barber (CoRR, 2020)

Bayesian Online Meta-Learning with Laplace Approximation. - Pau Ching Yap; Hippolyt Ritter; David Barber (CoRR, 2020)

Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders. - Benoit Gaujac; Ilya Feige; David Barber (CoRR, 2020)

Learning to Noise: Application-Agnostic Data Sharing with Local Differential Privacy. - Alex Mansbridge; Gregory Barbour; Davide Piras; Christopher Frye; Ilya Feige; David Barber (CoRR, 2020)

Validation of an EMR algorithm to measure the prevalence of ADHD in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). - Rachael Morkem; Kenneth Handelman; John A. Queenan; Richard Birtwhistle; David Barber (BMC Medical Informatics Decis. Mak., 2020)

Improving Gaussian mixture latent variable model convergence with Optimal Transport. - Benoit Gaujac; Ilya Feige; David Barber (ACML, 2021)

Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks. - Thomas Bird; Friso H. Kingma; David Barber (ICLR, 2021)

Addressing Catastrophic Forgetting in Few-Shot Problems. - Pau Ching Yap; Hippolyt Ritter; David Barber (ICML, 2021)

Learning Disentangled Representations with the Wasserstein Autoencoder. - Benoit Gaujac; Ilya Feige; David Barber (ECML/PKDD (3), 2021)

Locally-Contextual Nonlinear CRFs for Sequence Labeling. - Harshil Shah; Tim Z. Xiao; David Barber (CoRR, 2021)

Sample Efficient Model Evaluation. - Emine Yilmaz; Peter Hayes; Raza Habib; Jordan Burgess; David Barber (CoRR, 2021)

Adaptive Optimization with Examplewise Gradients. - Julius Kunze; James Townsend; David Barber (CoRR, 2021)

Prognostic Imaging Biomarker Discovery in Survival Analysis for Idiopathic Pulmonary Fibrosis. - 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; et al. (MICCAI (8), 2022)

Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data. - Ahmed H. Shahin; Joseph Jacob; Daniel C. Alexander; David Barber (MIDL, 2022)

Behavioral Segmentation for Enhanced Peer-to-Peer Patient Education. - Alessia Paglialonga; Rebecca Theal; David Barber; Robert Kyba; Aziz Guergachi; Karim Keshavjee (MIE, 2022)

Generalization Gap in Amortized Inference. - Mingtian Zhang; Peter Hayes; David Barber (NeurIPS, 2022)

Parallel Neural Local Lossless Compression. - Mingtian Zhang; James Townsend; Ning Kang 001; David Barber (CoRR, 2022)

Improving VAE-based Representation Learning. - Mingtian Zhang; Tim Z. Xiao; Brooks Paige; David Barber (CoRR, 2022)

Integrated Weak Learning. - Peter Hayes; Mingtian Zhang; Raza Habib; Jordan Burgess; Emine Yilmaz; David Barber (CoRR, 2022)

Towards Healing the Blindness of Score Matching. - Mingtian Zhang; Oscar Key; Peter Hayes; David Barber; Brooks Paige; François-Xavier Briol (CoRR, 2022)

SPaDe: A Synonym-based Pain-level Detection Tool for Osteoarthritis. - Yuhao Chen; Boqun Shu; Mojtaba Moattari; Farhana H. Zulkernine; John A. Queenan; David Barber (ICDH, 2023)

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

Smoothed Q-learning. - David Barber (CoRR, 2023)

A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study. - Yaozhi Lu; Shahab Aslani; An Zhao; Ahmed H. Shahin; David Barber; Mark Emberton; Daniel C. Alexander; Joseph Jacob (CoRR, 2023)

Generalized Multiple Intent Conditioned Slot Filling. - Harshil Shah; Arthur Wilcke; Marius Cobzarenco; Cristi Cobzarenco; Edward Challis; David Barber (CoRR, 2023)

Applying Patient Segmentation Using Primary Care Electronic Medical Records to Develop a Virtual Peer-to-Peer Intervention for Patients with Type 2 Diabetes. - Alessia Paglialonga; Rebecca Theal; Bruce Knox; Robert Kyba; David Barber; Aziz Guergachi; Karim Keshavjee (Future Internet, 2023)

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

Active Preference Learning for Large Language Models. - William Muldrew; Peter Hayes; Mingtian Zhang; David Barber (ICML, 2024)

Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning. - Mingtian Zhang; Shawn Lan; Peter Hayes; David Barber (CoRR, 2024)

Latent Attention for Linear Time Transformers. - Rares Dolga; Marius Cobzarenco; David Barber (CoRR, 2024)

Diffusion Model With Optimal Covariance Matching. - Zijing Ou; Mingtian Zhang; Andi Zhang 001; Tim Z. Xiao; Yingzhen Li; David Barber (CoRR, 2024)

RotRNN: Modelling Long Sequences with Rotations. - Rares Dolga; Kai Biegun; Jake Cunningham; David Barber (CoRR, 2024)

CenTime: Event-conditional modelling of censoring in survival analysis. - Ahmed H. Shahin; An Zhao; Alexander C. Whitehead; Daniel C. Alexander; Joseph Jacob; David Barber (Medical Image Anal., 2024)

Training Neural Samplers with Reverse Diffusive KL Divergence. - Jiajun He 003; Wenlin Chen; Mingtian Zhang; David Barber; José Miguel Hernández-Lobato (AISTATS, 2025)

Automatic Diagnosis of Hip and Knee Osteoarthritis from Medical Text Records. - Jiahao Cai; Vidhi Kokel; Farhana H. Zulkernine; John A. Queenan; David Barber (COMPSAC, 2025)

Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching. - Zijing Ou; Mingtian Zhang; Andi Zhang 001; Tim Z. Xiao; Yingzhen Li; David Barber (ICLR, 2025)

Towards Training One-Step Diffusion Models Without Distillation. - Mingtian Zhang; Jiajun He 003; Wenlin Chen; Zijing Ou; José Miguel Hernández-Lobato; Bernhard Schölkopf; David Barber (CoRR, 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 (CoRR, 2025)

Toward Autonomous UI Exploration: The UIExplorer Benchmark. - Andrei Cristian Nica; Akshaya Vishnu Kudlu Shanbhogue; Harshil Shah; Aleix Cambray; Tudor Berariu; Lucas Maystre; David Barber (CoRR, 2025)

Beyond Internal Data: Constructing Complete Datasets for Fairness Testing. - Varsha Ramineni; Hossein A. Rahmani; Emine Yilmaz; David Barber (CoRR, 2025)

Beyond Internal Data: Bounding and Estimating Fairness from Incomplete Data. - Varsha Ramineni; Hossein A. Rahmani; Emine Yilmaz; David Barber (CoRR, 2025)