David Barber - RankMe

158 papers A* 18A 7B 8C 3Journal 84Unranked 36

Year	Rank	Type	Title / Venue / Authors
2025	B	`conf`	Automatic Diagnosis of Hip and Knee Osteoarthritis from Medical Text Records. COMPSAC Jiahao Cai, Vidhi Kokel, Farhana H. Zulkernine, John A. Queenan, David Barber
2025	J	`jnl`	Beyond Internal Data: Bounding and Estimating Fairness from Incomplete Data. CoRR Varsha Ramineni, Hossein A. Rahmani, Emine Yilmaz, David Barber
2025	J	`jnl`	Beyond Internal Data: Constructing Complete Datasets for Fairness Testing. CoRR Varsha Ramineni, Hossein A. Rahmani, Emine Yilmaz, David Barber
2025	—	`conf`	From Characters to Tokens: Dynamic Grouping with Hierarchical BPE. EMNLP (Findings) Rares Dolga, Lucas Maystre, Tudor Berariu, David Barber
2025	J	`jnl`	From Characters to Tokens: Dynamic Grouping with Hierarchical BPE. CoRR Rares Dolga, Lucas Maystre, Tudor Berariu, David Barber
2025	A*	`conf`	Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching. ICLR Zijing Ou, Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yingzhen Li, David Barber
2025	J	`jnl`	Incremental Sequence Classification with Temporal Consistency. CoRR Lucas Maystre, Gabriel Barello, Tudor Berariu, Aleix Cambray, Rares Dolga, Alvaro Ortega Gonzalez, Andrei Cristian Nica, David Barber
2025	J	`jnl`	Toward Autonomous UI Exploration: The UIExplorer Benchmark. CoRR Andrei Cristian Nica, Akshaya Vishnu Kudlu Shanbhogue, Harshil Shah, Aleix Cambray, Tudor Berariu, Lucas Maystre, David Barber
2025	J	`jnl`	Towards Training One-Step Diffusion Models Without Distillation. CoRR Mingtian Zhang, Jiajun He, Wenlin Chen, Zijing Ou, José Miguel Hernández-Lobato, Bernhard Schölkopf, David Barber
2025	A	`conf`	Training Neural Samplers with Reverse Diffusive KL Divergence. AISTATS Jiajun He, Wenlin Chen, Mingtian Zhang, David Barber, José Miguel Hernández-Lobato
2025	J	`jnl`	Unifying Linear-Time Attention via Latent Probabilistic Modelling. Trans. Mach. Learn. Res. Rares Dolga, Lucas Maystre, Marius Cobzarenco, David Barber
2025	J	`jnl`	VarDiU: A Variational Diffusive Upper Bound for One-Step Diffusion Distillation. CoRR Leyang Wang, Mingtian Zhang, Zijing Ou, David Barber
2024	A*	`conf`	Active Preference Learning for Large Language Models. ICML William Muldrew, Peter Hayes, Mingtian Zhang, David Barber
2024	J	`jnl`	Active Preference Learning for Large Language Models. CoRR William Muldrew, Peter Hayes, Mingtian Zhang, David Barber
2024	J	`jnl`	CenTime: Event-conditional modelling of censoring in survival analysis. Medical Image Anal. Ahmed H. Shahin, An Zhao, Alexander C. Whitehead, Daniel C. Alexander, Joseph Jacob, David Barber
2024	J	`jnl`	Diffusion Model With Optimal Covariance Matching. CoRR Zijing Ou, Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yingzhen Li, David Barber
2024	A*	`conf`	Diffusive Gibbs Sampling. ICML Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber
2024	J	`jnl`	Diffusive Gibbs Sampling. CoRR Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber
2024	J	`jnl`	Latent Attention for Linear Time Transformers. CoRR Rares Dolga, Marius Cobzarenco, David Barber
2024	J	`jnl`	Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning. CoRR Mingtian Zhang, Shawn Lan, Peter Hayes, David Barber
2024	J	`jnl`	RotRNN: Modelling Long Sequences with Rotations. CoRR Rares Dolga, Kai Biegun, Jake Cunningham, David Barber
2024	J	`jnl`	Training Neural Samplers with Reverse Diffusive KL Divergence. CoRR Jiajun He, Wenlin Chen, Mingtian Zhang, David Barber, José Miguel Hernández-Lobato
2023	J	`jnl`	A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study. CoRR Yaozhi Lu, Shahab Aslani, An Zhao, Ahmed H. Shahin, David Barber, Mark Emberton, Daniel C. Alexander, Joseph Jacob
2023	J	`jnl`	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 Alessia Paglialonga, Rebecca Theal, Bruce Knox, Robert Kyba, David Barber, Aziz Guergachi, Karim Keshavjee
2023	J	`jnl`	CenTime: Event-Conditional Modelling of Censoring in Survival Analysis. CoRR Ahmed H. Shahin, An Zhao, Alexander C. Whitehead, Daniel C. Alexander, Joseph Jacob, David Barber
2023	J	`jnl`	Generalized Multiple Intent Conditioned Slot Filling. CoRR Harshil Shah, Arthur Wilcke, Marius Cobzarenco, Cristi Cobzarenco, Edward Challis, David Barber
2023	A*	`conf`	Moment Matching Denoising Gibbs Sampling. NeurIPS Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber
2023	J	`jnl`	Moment Matching Denoising Gibbs Sampling. CoRR Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber
2023	—	`conf`	SPaDe: A Synonym-based Pain-level Detection Tool for Osteoarthritis. ICDH Yuhao Chen, Boqun Shu, Mojtaba Moattari, Farhana H. Zulkernine, John A. Queenan, David Barber
2023	J	`jnl`	Smoothed Q-learning. CoRR David Barber
2022	—	`conf`	Behavioral Segmentation for Enhanced Peer-to-Peer Patient Education. MIE Alessia Paglialonga, Rebecca Theal, David Barber, Robert Kyba, Aziz Guergachi, Karim Keshavjee
2022	A*	`conf`	Generalization Gap in Amortized Inference. NeurIPS Mingtian Zhang, Peter Hayes, David Barber
2022	J	`jnl`	Generalization Gap in Amortized Inference. CoRR Mingtian Zhang, Peter Hayes, David Barber
2022	J	`jnl`	Improving VAE-based Representation Learning. CoRR Mingtian Zhang, Tim Z. Xiao, Brooks Paige, David Barber
2022	J	`jnl`	Integrated Weak Learning. CoRR Peter Hayes, Mingtian Zhang, Raza Habib, Jordan Burgess, Emine Yilmaz, David Barber
2022	J	`jnl`	Parallel Neural Local Lossless Compression. CoRR Mingtian Zhang, James Townsend, Ning Kang, David Barber
2022	—	`conf`	Prognostic Imaging Biomarker Discovery in Survival Analysis for Idiopathic Pulmonary Fibrosis. MICCAI (8) 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
2022	—	`conf`	Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data. MIDL Ahmed H. Shahin, Joseph Jacob, Daniel C. Alexander, David Barber
2022	J	`jnl`	Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data. CoRR Ahmed H. Shahin, Joseph Jacob, Daniel C. Alexander, David Barber
2022	J	`jnl`	Towards Healing the Blindness of Score Matching. CoRR Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, François-Xavier Briol
2021	J	`jnl`	Adaptive Optimization with Examplewise Gradients. CoRR Julius Kunze, James Townsend, David Barber
2021	A*	`conf`	Addressing Catastrophic Forgetting in Few-Shot Problems. ICML Pau Ching Yap, Hippolyt Ritter, David Barber
2021	C	`conf`	Improving Gaussian mixture latent variable model convergence with Optimal Transport. ACML Benoit Gaujac, Ilya Feige, David Barber
2021	—	`conf`	Learning Disentangled Representations with the Wasserstein Autoencoder. ECML/PKDD (3) Benoit Gaujac, Ilya Feige, David Barber
2021	J	`jnl`	Locally-Contextual Nonlinear CRFs for Sequence Labeling. CoRR Harshil Shah, Tim Z. Xiao, David Barber
2021	A*	`conf`	Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks. ICLR Thomas Bird, Friso H. Kingma, David Barber
2021	J	`jnl`	Sample Efficient Model Evaluation. CoRR Emine Yilmaz, Peter Hayes, Raza Habib, Jordan Burgess, David Barber
2020	J	`jnl`	Bayesian Online Meta-Learning with Laplace Approximation. CoRR Pau Ching Yap, Hippolyt Ritter, David Barber
2020	A*	`conf`	HiLLoC: lossless image compression with hierarchical latent variable models. ICLR James Townsend, Thomas Bird, Julius Kunze, David Barber
2020	J	`jnl`	Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders. CoRR Benoit Gaujac, Ilya Feige, David Barber
2020	J	`jnl`	Learning disentangled representations with the Wasserstein Autoencoder. CoRR Benoit Gaujac, Ilya Feige, David Barber
2020	J	`jnl`	Learning to Noise: Application-Agnostic Data Sharing with Local Differential Privacy. CoRR Alex Mansbridge, Gregory Barbour, Davide Piras, Christopher Frye, Ilya Feige, David Barber
2020	J	`jnl`	Private Machine Learning via Randomised Response. CoRR David Barber
2020	J	`jnl`	Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks. CoRR Thomas Bird, Friso H. Kingma, David Barber
2020	A*	`conf`	Spread Divergence. ICML Mingtian Zhang, Peter Hayes, Thomas Bird, Raza Habib, David Barber
2020	J	`jnl`	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. Rachael Morkem, Kenneth Handelman, John A. Queenan, Richard Birtwhistle, David Barber
2019	—	`conf`	Auxiliary Variational MCMC. ICLR (Poster) Raza Habib, David Barber
2019	J	`jnl`	Gaussian Mean Field Regularizes by Limiting Learned Information. CoRR Julius Kunze, Louis Kirsch, Hippolyt Ritter, David Barber
2019	J	`jnl`	Gaussian Mean Field Regularizes by Limiting Learned Information. Entropy Julius Kunze, Louis Kirsch, Hippolyt Ritter, David Barber
2019	J	`jnl`	HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Models. CoRR James Townsend, Thomas Bird, Julius Kunze, David Barber
2019	J	`jnl`	Improving latent variable descriptiveness by modelling rather than ad-hoc factors. Mach. Learn. Alex Mansbridge, Roberto Fierimonte, Ilya Feige, David Barber
2019	J	`jnl`	Practical Lossless Compression with Latent Variables using Bits Back Coding. CoRR James Townsend, Tom Bird, David Barber
2019	—	`conf`	Practical lossless compression with latent variables using bits back coding. ICLR (Poster) James Townsend, Thomas Bird, David Barber
2019	A*	`conf`	Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers. CVPR Zhen He, Jian Li, Daxue Liu, Hangen He, David Barber
2019	J	`jnl`	Variational f-divergence Minimization. CoRR Mingtian Zhang, Thomas Bird, Raza Habib, Tianlin Xu, David Barber
2018	—	`conf`	A Scalable Laplace Approximation for Neural Networks. ICLR (Poster) Hippolyt Ritter, Aleksandar Botev, David Barber
2018	—	`conf`	Detecting Low Back Pain from Clinical Narratives Using Machine Learning Approaches. DEXA Workshops Michael Judd, Farhana H. Zulkernine, Brent Wolfrom, David Barber, Akshay Rajaram
2018	J	`jnl`	Gaussian mixture models with Wasserstein distance. CoRR Benoit Gaujac, Ilya Feige, David Barber
2018	A*	`conf`	Generating Sentences Using a Dynamic Canvas. AAAI Harshil Shah, Bowen Zheng, David Barber
2018	J	`jnl`	Generating Sentences Using a Dynamic Canvas. CoRR Harshil Shah, Bowen Zheng, David Barber
2018	A*	`conf`	Generative Neural Machine Translation. NeurIPS Harshil Shah, David Barber
2018	J	`jnl`	Generative Neural Machine Translation. CoRR Harshil Shah, David Barber
2018	J	`jnl`	Improving latent variable descriptiveness with AutoGen. CoRR Alex Mansbridge, Roberto Fierimonte, Ilya Feige, David Barber
2018	A*	`conf`	Modular Networks: Learning to Decompose Neural Computation. NeurIPS Louis Kirsch, Julius Kunze, David Barber
2018	J	`jnl`	Modular Networks: Learning to Decompose Neural Computation. CoRR Louis Kirsch, Julius Kunze, David Barber
2018	J	`jnl`	Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting. CoRR Hippolyt Ritter, Aleksandar Botev, David Barber
2018	A*	`conf`	Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting. NeurIPS Hippolyt Ritter, Aleksandar Botev, David Barber
2018	J	`jnl`	Spread Divergences. CoRR David Barber, Mingtian Zhang, Raza Habib, Thomas Bird
2018	J	`jnl`	Stochastic Variational Optimization. CoRR Thomas Bird, Julius Kunze, David Barber
2018	J	`jnl`	Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers. CoRR Zhen He, Jian Li, Daxue Liu, Hangen He, David Barber
2017	A	`conf`	Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification. AISTATS Aleksandar Botev, Bowen Zheng, David Barber
2017	B	`conf`	Nesterov's accelerated gradient and momentum as approximations to regularised update descent. IJCNN Aleksandar Botev, Guy Lever, David Barber
2017	B	`conf`	Overdispersed variational autoencoders. IJCNN Harshil Shah, David Barber, Aleksandar Botev
2017	A*	`conf`	Practical Gauss-Newton Optimisation for Deep Learning. ICML Aleksandar Botev, Hippolyt Ritter, David Barber
2017	—	`conf`	Thinking Fast and Slow with Deep Learning and Tree Search. NIPS Thomas Anthony, Zheng Tian, David Barber
2017	J	`jnl`	Thinking Fast and Slow with Deep Learning and Tree Search. CoRR Thomas Anthony, Zheng Tian, David Barber
2017	—	`conf`	Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning. NIPS Zhen He, Shaobing Gao, Liang Xiao, Daxue Liu, Hangen He, David Barber
2017	J	`jnl`	Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning. CoRR Zhen He, Shaobing Gao, Liang Xiao, Daxue Liu, Hangen He, David Barber
2016	J	`jnl`	Approximate Newton Methods for Policy Search in Markov Decision Processes. J. Mach. Learn. Res. Thomas Furmston, Guy Lever, David Barber
2016	J	`jnl`	Nesterov's Accelerated Gradient and Momentum as approximations to Regularised Update Descent. CoRR Aleksandar Botev, Guy Lever, David Barber
2016	—	`conf`	Using machine learning to predict hypertension from a clinical dataset. SSCI Daniel Lafrenière, Farhana H. Zulkernine, David Barber, Ken Martin
2016	J	`jnl`	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. Suzanne Biro, Tyler S. Williamson, Jannet Ann Leggett, David Barber, Rachael Morkem, Kieran Moore, Paul Belanger, Brian Mosley, Ian Janssen
2015	J	`jnl`	Topic factor models: Uncovering thematic structure in equity market data. Intell. Data Anal. Joe Staines, David Barber
2014	J	`jnl`	An Image Reconstruction Algorithm for 3-D Electrical Impedance Mammography. IEEE Trans. Medical Imaging Xiaolin Zhang, Wei Wang, Gerald Sze, David Barber, Chris R. Chatwin
2014	A*	`conf`	Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations. ICML David Barber, Yali Wang
2014	—	`conf`	Keep Taking the Tablets: Integrating the Mobile in Work-Based Learning. BIIML David Barber
2014	J	`jnl`	On solving Ordinary Differential Equations using Gaussian Processes. CoRR David Barber
2013	J	`jnl`	Gaussian Kullback-Leibler approximate inference. J. Mach. Learn. Res. Edward Challis, David Barber
2013	B	`conf`	Optimization by Variational Bounding. ESANN Joe Staines, David Barber
2012	—	`conf`	A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes. NIPS Thomas Furmston, David Barber
2012	—	`conf`	Affine Independent Variational Inference. NIPS Edward Challis, David Barber
2012	A*	`conf`	Bayesian Conditional Cointegration. ICML Chris Bracegirdle, David Barber
2012	—	`book`	Bayesian reasoning and machine learning. David Barber
2012	J	`jnl`	Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite Matrices CoRR David Barber
2012	J	`jnl`	Efficient Inference in Markov Control Problems CoRR Thomas Furmston, David Barber
2012	J	`jnl`	On the Computational Complexity of Stochastic Controller Optimization in POMDPs. ACM Trans. Comput. Theory Nikos Vlassis, Michael L. Littman, David Barber
2012	J	`jnl`	Variational Optimization CoRR Joe Staines, David Barber
2011	A	`conf`	Concave Gaussian Variational Approximations for Inference in Large-Scale Bayesian Linear Models. AISTATS Edward Challis, David Barber
2011	A	`conf`	Efficient Inference in Markov Control Problems. UAI Thomas Furmston, David Barber
2011	—	`conf`	Lagrange Dual Decomposition for Finite Horizon Markov Decision Processes. ECML/PKDD (1) Thomas Furmston, David Barber
2011	J	`jnl`	On the computational complexity of stochastic controller optimization in POMDPs CoRR Nikos Vlassis, Michael L. Littman, David Barber
2011	A	`conf`	Switch-Reset Models : Exact and Approximate Inference. AISTATS Chris Bracegirdle, David Barber
2011	J	`jnl`	Variational Cumulant Expansions for Intractable Distributions CoRR David Barber, Piërre van de Laar
2010	J	`jnl`	Graphical Models for Time-Series. IEEE Signal Process. Mag. David Barber, A. Taylan Cemgil
2010	A	`conf`	Variational methods for Reinforcement Learning. AISTATS Thomas Furmston, David Barber
2009	J	`jnl`	A Simple Alternative Derivation of the Expectation Correction Algorithm. IEEE Signal Process. Lett. Bertrand Mesot, David Barber
2008	A	`conf`	Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite Matrices. UAI David Barber
2007	—	`conf`	A Bayesian Alternative to Gain Adaptation in Autoregressive Hidden Markov Models. ICASSP (2) Bertrand Mesot, David Barber
2007	J	`jnl`	Bayesian Factorial Linear Gaussian State-Space Models for Biosignal Decomposition. IEEE Signal Process. Lett. Silvia Chiappa, David Barber
2007	—	`conf`	Stable Belief Propagation in Gaussian Dags. ICASSP (2) David Barber, Peter Sollich
2007	J	`jnl`	Switching Linear Dynamical Systems for Noise Robust Speech Recognition. IEEE Trans. Speech Audio Process. Bertrand Mesot, David Barber
2006	—	`conf`	A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems. NIPS David Barber, Bertrand Mesot
2006	J	`jnl`	A generative model for music transcription. IEEE Trans. Speech Audio Process. Ali Taylan Cemgil, Hilbert J. Kappen, David Barber
2006	J	`jnl`	EEG classification using generative independent component analysis. Neurocomputing Silvia Chiappa, David Barber
2006	—	`conf`	Efficient Kalman Smoothing for Harmonic State-Space Models. ICASSP (3) David Barber
2006	J	`jnl`	Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems. J. Mach. Learn. Res. David Barber
2006	J	`jnl`	Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning. Neural Comput. Jean-Pascal Pfister, Taro Toyoizumi, David Barber, Wulfram Gerstner
2006	—	`conf`	Tagging of name records for genealogical data browsing. JCDL Mike Perrow, David Barber
2006	—	`conf`	Unified Inference for Variational Bayesian Linear Gaussian State-Space Models. NIPS David Barber, Silvia Chiappa
2005	A*	`conf`	A graphical model for chord progressions embedded in a psychoacoustic space. ICML Jean-François Paiement, Douglas Eck, Samy Bengio, David Barber
2005	—	`conf`	Auxiliary Variational Information Maximization for Dimensionality Reduction. SLSFS Felix V. Agakov, David Barber
2005	J	`jnl`	Islands of the Arctic. Cartogr. Int. J. Geogr. Inf. Geovisualization David Barber
2005	—	`conf`	Kernelized Infomax Clustering. NIPS Felix V. Agakov, David Barber
2005	B	`conf`	generative independent component analysis for EEG classification. ESANN Silvia Chiappa, David Barber
2004	B	`conf`	An Auxiliary Variational Method. ICONIP Felix V. Agakov, David Barber
2004	B	`conf`	Variational Information Maximization for Neural Coding. ICONIP Felix V. Agakov, David Barber
2003	C	`conf`	Approximate Learning in Temporal Hidden Hopfield Models. ICANN Felix V. Agakov, David Barber
2003	—	`conf`	Information Maximization in Noisy Channels : A Variational Approach. NIPS David Barber, Felix V. Agakov
2003	C	`conf`	Optimal Hebbian Learning: A Probabilistic Point of View. ICANN Jean-Pascal Pfister, David Barber, Wulfram Gerstner
2002	—	`conf`	Dynamic Bayesian Networks with Deterministic Latent Tables. NIPS David Barber
2002	—	`conf`	Learning in Spiking Neural Assemblies. NIPS David Barber
2001	J	`jnl`	Deterministic Generative Models for Fast Feature Discovery. Data Min. Knowl. Discov. Machiel Westerdijk, David Barber, Wim Wiegerinck
1999	—	`conf`	Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks. NIPS David Barber, Peter Sollich
1999	J	`jnl`	Variational Cumulant Expansions for Intractable Distributions. J. Artif. Intell. Res. David Barber, Piërre van de Laar
1998	J	`jnl`	Bayesian Classification With Gaussian Processes. IEEE Trans. Pattern Anal. Mach. Intell. Christopher K. I. Williams, David Barber
1998	J	`jnl`	Online Learning from Finite Training Sets and Robustness to Input Bias. Neural Comput. Peter Sollich, David Barber
1998	—	`conf`	Tractable Variational Structures for Approximating Graphical Models. NIPS David Barber, Wim Wiegerinck
1997	—	`conf`	Ensemble Learning for Multi-Layer Networks. NIPS David Barber, Christopher M. Bishop
1997	J	`jnl`	OhioLINK: A Consortial Approach to Digital Library Management. D Lib Mag. David Barber
1997	—	`conf`	On-line Learning from Finite Training Sets in Nonlinear Networks. NIPS Peter Sollich, David Barber
1997	—	`conf`	Radial Basis Functions: A Bayesian Treatment. NIPS David Barber, Bernhard Schottky
1996	—	`conf`	Bayesian Model Comparison by Monte Carlo Chaining. NIPS David Barber, Christopher M. Bishop
1996	J	`jnl`	Does Extra Knowledge Necessarily Improve Generalization? Neural Comput. David Barber, David Saad
1996	—	`—`	Finite size effects in neural network algorithms. David Barber
1996	—	`conf`	Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo. NIPS David Barber, Christopher K. I. Williams
1996	—	`conf`	Online Learning from Finite Training Sets: An Analytical Case Study. NIPS Peter Sollich, David Barber
1995	B	`conf`	Knowledge and generalisation in simple learning systems. ESANN David Barber, David Saad
1995	J	`jnl`	Test Error Fluctuations in Finite Linear Perceptrons. Neural Comput. David Barber, David Saad, Peter Sollich