Zoubin Ghahramani

According to our database1, Zoubin Ghahramani authored at least 285 papers between 1993 and 2018.

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Bibliography

2018
Denotational validation of higher-order Bayesian inference.
PACMPL, 2018

Functional programming for modular Bayesian inference.
PACMPL, 2018

Probabilistic Meta-Representations Of Neural Networks.
CoRR, 2018

Automatic Bayesian Density Analysis.
CoRR, 2018

Handling Incomplete Heterogeneous Data using VAEs.
CoRR, 2018

Variational Bayesian dropout: pitfalls and fixes.
CoRR, 2018

Antithetic and Monte Carlo kernel estimators for partial rankings.
CoRR, 2018

Probabilistic Deep Learning using Random Sum-Product Networks.
CoRR, 2018

Variational Measure Preserving Flows.
CoRR, 2018

Gaussian Process Behaviour in Wide Deep Neural Networks.
CoRR, 2018

The Mirage of Action-Dependent Baselines in Reinforcement Learning.
CoRR, 2018

Weakly supervised collective feature learning from curated media.
CoRR, 2018

Imitation networks: Few-shot learning of neural networks from scratch.
CoRR, 2018

Branch-recombinant Gaussian processes for analysis of perturbations in biological time series.
Bioinformatics, 2018

The Mirage of Action-Dependent Baselines in Reinforcement Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018

Variational Bayesian dropout: pitfalls and fixes.
Proceedings of the 35th International Conference on Machine Learning, 2018

Discovering Interpretable Representations for Both Deep Generative and Discriminative Models.
Proceedings of the 35th International Conference on Machine Learning, 2018

Few-shot learning of neural networks from scratch by pseudo example optimization.
Proceedings of the British Machine Vision Conference 2018, 2018

Turing: Composable inference for probabilistic programming.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Weakly Supervised Collective Feature Learning From Curated Media.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
GPflow: A Gaussian Process Library using TensorFlow.
Journal of Machine Learning Research, 2017

Denotational validation of higher-order Bayesian inference.
CoRR, 2017

General Latent Feature Modeling for Data Exploration Tasks.
CoRR, 2017

Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning.
CoRR, 2017

Deep Bayesian Active Learning with Image Data.
CoRR, 2017

One-Shot Learning in Discriminative Neural Networks.
CoRR, 2017

Lost Relatives of the Gumbel Trick.
CoRR, 2017

Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Automatic Discovery of the Statistical Types of Variables in a Dataset.
Proceedings of the 34th International Conference on Machine Learning, 2017

Magnetic Hamiltonian Monte Carlo.
Proceedings of the 34th International Conference on Machine Learning, 2017

A Birth-Death Process for Feature Allocation.
Proceedings of the 34th International Conference on Machine Learning, 2017

Bayesian inference on random simple graphs with power law degree distributions.
Proceedings of the 34th International Conference on Machine Learning, 2017

Deep Bayesian Active Learning with Image Data.
Proceedings of the 34th International Conference on Machine Learning, 2017

Lost Relatives of the Gumbel Trick.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
Human Activity Recognition by Combining a Small Number of Classifiers.
IEEE J. Biomedical and Health Informatics, 2016

Unsupervised Many-to-Many Object Matching for Relational Data.
IEEE Trans. Pattern Anal. Mach. Intell., 2016

A General Framework for Constrained Bayesian Optimization using Information-based Search.
Journal of Machine Learning Research, 2016

Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic.
CoRR, 2016

A study of the effect of JPG compression on adversarial images.
CoRR, 2016

Markov Beta Processes for Time Evolving Dictionary Learning.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

The Mondrian Kernel.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Distributed Flexible Nonlinear Tensor Factorization.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Pareto Frontier Learning with Expensive Correlated Objectives.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Scalable Discrete Sampling as a Multi-Armed Bandit Problem.
Proceedings of the 33nd International Conference on Machine Learning, 2016

On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Bayesian Generalised Ensemble Markov Chain Monte Carlo.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
A Very Simple Safe-Bayesian Random Forest.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Relational Learning and Network Modelling Using Infinite Latent Attribute Models.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Pitman Yor Diffusion Trees for Bayesian Hierarchical Clustering.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

GPstruct: Bayesian Structured Prediction Using Gaussian Processes.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Variational Infinite Hidden Conditional Random Fields.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Probabilistic machine learning and artificial intelligence.
Nature, 2015

Linear dimensionality reduction: survey, insights, and generalizations.
Journal of Machine Learning Research, 2015

An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process.
CoRR, 2015

Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions.
CoRR, 2015

Slice Sampling for Probabilistic Programming.
CoRR, 2015

Neural Adaptive Sequential Monte Carlo.
CoRR, 2015

Sandwiching the marginal likelihood using bidirectional Monte Carlo.
CoRR, 2015

Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference.
CoRR, 2015

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning.
CoRR, 2015

Training generative neural networks via Maximum Mean Discrepancy optimization.
CoRR, 2015

Subsampling-Based Approximate Monte Carlo for Discrete Distributions.
CoRR, 2015

Training generative neural networks via Maximum Mean Discrepancy optimization.
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Particle Gibbs for Infinite Hidden Markov Models.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Statistical Model Criticism using Kernel Two Sample Tests.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

MCMC for Variationally Sparse Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Neural Adaptive Sequential Monte Carlo.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Predictive Entropy Search for Bayesian Optimization with Unknown Constraints.
Proceedings of the 32nd International Conference on Machine Learning, 2015

A Probabilistic Model for Dirty Multi-task Feature Selection.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Distributed Inference for Dirichlet Process Mixture Models.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Practical probabilistic programming with monads.
Proceedings of the 8th ACM SIGPLAN Symposium on Haskell, 2015

Scalable Variational Gaussian Process Classification.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
Partial Membership and Factor Analysis.
Proceedings of the Handbook of Mixed Membership Models and Their Applications., 2014

Student-t Processes as Alternatives to Gaussian Processes.
CoRR, 2014

Randomized Nonlinear Component Analysis.
CoRR, 2014

Automatic Construction and Natural-Language Description of Nonparametric Regression Models.
CoRR, 2014

Warped Mixtures for Nonparametric Cluster Shapes.
CoRR, 2014

Predictive Entropy Search for Efficient Global Optimization of Black-box Functions.
CoRR, 2014

Avoiding pathologies in very deep networks.
CoRR, 2014

The Random Forest Kernel and other kernels for big data from random partitions.
CoRR, 2014

Gaussian Process Volatility Model.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

General Table Completion using a Bayesian Nonparametric Model.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Predictive Entropy Search for Efficient Global Optimization of Black-box Functions.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Randomized Nonlinear Component Analysis.
Proceedings of the 31th International Conference on Machine Learning, 2014

A reversible infinite HMM using normalised random measures.
Proceedings of the 31th International Conference on Machine Learning, 2014

Cold-start Active Learning with Robust Ordinal Matrix Factorization.
Proceedings of the 31th International Conference on Machine Learning, 2014

Probabilistic Matrix Factorization with Non-random Missing Data.
Proceedings of the 31th International Conference on Machine Learning, 2014

Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices.
Proceedings of the 31th International Conference on Machine Learning, 2014

Beta Diffusion Trees.
Proceedings of the 31th International Conference on Machine Learning, 2014

Pitfalls in the use of Parallel Inference for the Dirichlet Process.
Proceedings of the 31th International Conference on Machine Learning, 2014

Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications.
Proceedings of the 31th International Conference on Machine Learning, 2014

Student-t Processes as Alternatives to Gaussian Processes.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Avoiding pathologies in very deep networks.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

A Non-parametric Conditional Factor Regression Model for Multi-Dimensional Input and Response.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Automatic Construction and Natural-Language Description of Nonparametric Regression Models.
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013
Model Reductions for Inference: Generality of Pairwise, Binary, and Planar Factor Graphs.
Neural Computation, 2013

Scaling the Indian Buffet Process via Submodular Maximization
CoRR, 2013

Structure Discovery in Nonparametric Regression through Compositional Kernel Search
CoRR, 2013

Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering.
CoRR, 2013

The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models.
CoRR, 2013

Bayesian Structured Prediction Using Gaussian Processes.
CoRR, 2013

Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering.
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models.
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Warped Mixtures for Nonparametric Cluster Shapes.
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Variational Hidden Conditional Random Fields with Coupled Dirichlet Process Mixtures.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

SIGMa: simple greedy matching for aligning large knowledge bases.
Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

Discovering latent influence in online social activities via shared cascade poisson processes.
Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

Dynamic Covariance Models for Multivariate Financial Time Series.
Proceedings of the 30th International Conference on Machine Learning, 2013

Scaling the Indian Buffet Process via Submodular Maximization.
Proceedings of the 30th International Conference on Machine Learning, 2013

Gaussian Process Vine Copulas for Multivariate Dependence.
Proceedings of the 30th International Conference on Machine Learning, 2013

Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks.
Proceedings of the 30th International Conference on Machine Learning, 2013

Structure Discovery in Nonparametric Regression through Compositional Kernel Search.
Proceedings of the 30th International Conference on Machine Learning, 2013

Active Learning for Interactive Visualization.
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

A Generative Model of Vector Space Semantics.
Proceedings of the Workshop on Continuous Vector Space Models and their Compositionality, 2013

2012
Flexible Martingale Priors for Deep Hierarchies.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

A Nonparametric Bayesian Model for Multiple Clustering with Overlapping Feature Views.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Bayesian Classifier Combination.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Gaussian Processes for time-marked time-series data.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

On the Convergence of Bound Optimization Algorithms
CoRR, 2012

SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases
CoRR, 2012

Bayesian Learning in Undirected Graphical Models: Approximate MCMC algorithms
CoRR, 2012

Copula-based Kernel Dependency Measures
CoRR, 2012

Correlated Non-Parametric Latent Feature Models
CoRR, 2012

A Non-Parametric Bayesian Method for Inferring Hidden Causes.
CoRR, 2012

Variable noise and dimensionality reduction for sparse Gaussian processes.
CoRR, 2012

Bayesian Inference for Gaussian Mixed Graph Models.
CoRR, 2012

Bayesian correlated clustering to integrate multiple datasets.
Bioinformatics, 2012

Modelling Input Varying Correlations between Multiple Responses.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Continuous Relaxations for Discrete Hamiltonian Monte Carlo.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Active Learning of Model Evidence Using Bayesian Quadrature.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Random function priors for exchangeable arrays with applications to graphs and relational data.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

A nonparametric variable clustering model.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Collaborative Gaussian Processes for Preference Learning.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Gaussian Process Regression Networks.
Proceedings of the 29th International Conference on Machine Learning, 2012

Copula-based Kernel Dependency Measures.
Proceedings of the 29th International Conference on Machine Learning, 2012

An Infinite Latent Attribute Model for Network Data.
Proceedings of the 29th International Conference on Machine Learning, 2012

Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning .
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
State of the Journal.
IEEE Trans. Pattern Anal. Mach. Intell., 2011

Editorial.
IEEE Trans. Pattern Anal. Mach. Intell., 2011

Editor's Note.
IEEE Trans. Pattern Anal. Mach. Intell., 2011

Approximate inference for the loss-calibrated Bayesian.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

The Indian Buffet Process: An Introduction and Review.
Journal of Machine Learning Research, 2011

Bayesian Active Learning for Classification and Preference Learning
CoRR, 2011

Bayesian and L1 Approaches to Sparse Unsupervised Learning
CoRR, 2011

Generalised Wishart Processes.
Proceedings of the UAI 2011, 2011

Pitman-Yor Diffusion Trees.
Proceedings of the UAI 2011, 2011

The Dynamic Beamformer.
Proceedings of the Machine Learning and Interpretation in Neuroimaging, 2011

Testing a Bayesian Measure of Representativeness Using a Large Image Database.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Message Passing Algorithms for the Dirichlet Diffusion Tree.
Proceedings of the 28th International Conference on Machine Learning, 2011

A Comparison of Human and Agent Reinforcement Learning in Partially Observable Domains.
Proceedings of the 33th Annual Meeting of the Cognitive Science Society, 2011

2010
Editor's Note.
IEEE Trans. Pattern Anal. Mach. Intell., 2010

Editor's Note.
IEEE Trans. Pattern Anal. Mach. Intell., 2010

Editor's Note.
IEEE Trans. Pattern Anal. Mach. Intell., 2010

Dependent Indian Buffet Processes.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Kronecker Graphs: An Approach to Modeling Networks.
Journal of Machine Learning Research, 2010

Learning the Structure of Deep Sparse Graphical Models.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series.
Journal of Computational Biology, 2010

Nonparametric Bayesian Sparse Factor Models with application to Gene Expression modelling
CoRR, 2010

Discovering transcriptional modules by Bayesian data integration.
Bioinformatics [ISMB], 2010

Gene function prediction from synthetic lethality networks via ranking on demand.
Bioinformatics, 2010

Copula Processes.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Tree-Structured Stick Breaking for Hierarchical Data.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Probabilistic graphical models for semi-supervised traffic classification.
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference, 2010

(Invited Talk) Bayesian Hidden Markov Models and Extensions.
Proceedings of the Fourteenth Conference on Computational Natural Language Learning, 2010

Scaling the iHMM: Parallelization versus Hadoop.
Proceedings of the 10th IEEE International Conference on Computer and Information Technology, 2010

2009
Modeling and Visualizing Uncertainty in Gene Expression Clusters Using Dirichlet Process Mixtures.
IEEE/ACM Trans. Comput. Biology Bioinform., 2009

Introduction of New Associate Editors.
IEEE Trans. Pattern Anal. Mach. Intell., 2009

Introduction of New Associate Editors.
IEEE Trans. Pattern Anal. Mach. Intell., 2009

Tree-Based Inference for Dirichlet Process Mixtures.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

The Block Diagonal Infinite Hidden Markov Model.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Factorial Mixture of Gaussians and the Marginal Independence Model.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models.
Journal of Machine Learning Research, 2009

A kernel method for unsupervised structured network inference.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Choosing a Variable to Clamp.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Probabilistic Models for Incomplete Multi-dimensional Arrays.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Ranking Relations using Analogies in Biological and Information Networks
CoRR, 2009

Bayesian two-sample tests
CoRR, 2009

R/BHC: fast Bayesian hierarchical clustering for microarray data.
BMC Bioinformatics, 2009

Correlated Non-Parametric Latent Feature Models.
Proceedings of the UAI 2009, 2009

A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series.
Proceedings of the Research in Computational Molecular Biology, 2009

Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Accelerated sampling for the Indian Buffet Process.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Archipelago: nonparametric Bayesian semi-supervised learning.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Discovering Temporal Patterns of Differential Gene Expression in Microarray Time Series.
Proceedings of the German Conference on Bioinformatics 2009, 2009

The infinite HMM for unsupervised PoS tagging.
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 2009

2008
Second-Order Latent-Space Variational Bayes for Approximate Bayesian Inference.
IEEE Signal Process. Lett., 2008

Latent-Space Variational Bayes.
IEEE Trans. Pattern Anal. Mach. Intell., 2008

Introduction of New Associate Editors.
IEEE Trans. Pattern Anal. Mach. Intell., 2008

Introduction of New Associate Editors.
IEEE Trans. Pattern Anal. Mach. Intell., 2008

Introduction of New Associate Editors.
IEEE Trans. Pattern Anal. Mach. Intell., 2008

Editorial-State of the Transactions.
IEEE Trans. Pattern Anal. Mach. Intell., 2008

Flexible latent variable models for multi-task learning.
Machine Learning, 2008

Kronecker Graphs: An Approach to Modeling Networks.
CoRR, 2008

Outlier Robust Gaussian Process Classification.
Proceedings of the Structural, 2008

Bayesian Exponential Family PCA.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

The Infinite Factorial Hidden Markov Model.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Statistical models for partial membership.
Proceedings of the Machine Learning, 2008

Beam sampling for the infinite hidden Markov model.
Proceedings of the Machine Learning, 2008

Metropolis Algorithms for Representative Subgraph Sampling.
Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 2008

Bayesian Methods for Artificial Intelligence and Machine Learning.
Proceedings of the ECAI 2008, 2008

2007
Stick-breaking Construction for the Indian Buffet Process.
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Local and global sparse Gaussian process approximations.
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Analogical Reasoning with Relational Bayesian Sets.
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

A Nonparametric Bayesian Approach to Modeling Overlapping Clusters.
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Hidden Common Cause Relations in Relational Learning.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Infinite Sparse Factor Analysis and Infinite Independent Components Analysis.
Proceedings of the Independent Component Analysis and Signal Separation, 2007

2006
Bayesian Segmental Models with Multiple Sequence Alignment Profiles for Protein Secondary Structure and Contact Map Prediction.
IEEE/ACM Trans. Comput. Biology Bioinform., 2006

Appearance-based gender classification with Gaussian processes.
Pattern Recognition Letters, 2006

Bayesian Gaussian Process Classification with the EM-EP Algorithm.
IEEE Trans. Pattern Anal. Mach. Intell., 2006

A Non-Parametric Bayesian Method for Inferring Hidden Causes.
Proceedings of the UAI '06, 2006

Variable Noise and Dimensionality Reduction for Sparse Gaussian processes.
Proceedings of the UAI '06, 2006

Bayesian Inference for Gaussian Mixed Graph Models.
Proceedings of the UAI '06, 2006

MCMC for Doubly-intractable Distributions.
Proceedings of the UAI '06, 2006

Identifying Protein Complexes in High-Throughput Protein Interaction Screens Using an Infinite Latent Feature Model.
Proceedings of the Biocomputing 2006, 2006

Modeling Dyadic Data with Binary Latent Factors.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Relational Learning with Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Gender Classification with Bayesian Kernel Methods.
Proceedings of the International Joint Conference on Neural Networks, 2006

A new approach to data driven clustering.
Proceedings of the Machine Learning, 2006

Face Recognition Based on Separable Lattice HMMS.
Proceedings of the 2006 IEEE International Conference on Acoustics Speech and Signal Processing, 2006

A Simple Bayesian Framework for Content-Based Image Retrieval.
Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), 2006

Spectral Methods for Automatic Multiscale Data Clustering.
Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), 2006

2005
Gaussian Processes for Ordinal Regression.
Journal of Machine Learning Research, 2005

Biomarker discovery in microarray gene expression data with Gaussian processes.
Bioinformatics, 2005

A Bayesian approach to reconstructing genetic regulatory networks with hidden factors.
Bioinformatics, 2005

Learning Multiple Related Tasks using Latent Independent Component Analysis.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Sparse Gaussian Processes using Pseudo-inputs.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Nested sampling for Potts models.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Infinite latent feature models and the Indian buffet process.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Bayesian Sets.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Compact approximations to Bayesian predictive distributions.
Proceedings of the Machine Learning, 2005

Bayesian hierarchical clustering.
Proceedings of the Machine Learning, 2005

Preference learning with Gaussian processes.
Proceedings of the Machine Learning, 2005

U-Likelihood and U-Updating Algorithms: Statistical Inference in Latent Variable Models.
Proceedings of the Machine Learning: ECML 2005, 2005

2004
Simultaneous Localization and Mapping with Sparse Extended Information Filters.
I. J. Robotics Res., 2004

Modeling T-cell activation using gene expression profiling and state-space models.
Bioinformatics, 2004

Bayesian Learning in Undirected Graphical Models: Approximate MCMC Algorithms.
Proceedings of the UAI '04, 2004

Clustering Protein Sequence and Structure Space with Infinite Gaussian Mixture Models.
Proceedings of the Biocomputing 2004, 2004

The Status of Structural Genomics Defined Through the Analysis of Current Targets and Structures.
Proceedings of the Biocomputing 2004, 2004

Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

A Probabilistic Model for Online Document Clustering with Application to Novelty Detection.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Predictive automatic relevance determination by expectation propagation.
Proceedings of the Machine Learning, 2004

A graphical model for protein secondary structure prediction.
Proceedings of the Machine Learning, 2004

Protein secondary structure prediction using sigmoid belief networks to parameterize segmental semi-Markov models.
Proceedings of the ESANN 2004, 2004

2003
On the Convergence of Bound Optimization Algorithms.
Proceedings of the UAI '03, 2003

Warped Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions.
Proceedings of the Machine Learning, 2003

Optimization with EM and Expectation-Conjugate-Gradient.
Proceedings of the Machine Learning, 2003

Unsupervised Learning.
Proceedings of the Advanced Lectures on Machine Learning, 2003

2002
Bayesian model search for mixture models based on optimizing variational bounds.
Neural Networks, 2002

A Bayesian network model for protein fold and remote homologue recognition.
Bioinformatics, 2002

Simultaneous Mapping and Localization with Sparse Extended Information Filters: Theory and Initial Results.
Proceedings of the Algorithmic Foundations of Robotics V, 2002

Bayesian Monte Carlo.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Learning with Multiple Labels.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

2001
An Introduction to Hidden Markov Models and Bayesian Networks.
IJPRAI, 2001

Infinite Mixtures of Gaussian Process Experts.
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

The Infinite Hidden Markov Model.
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

2000
Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates.
VLSI Signal Processing, 2000

SMEM Algorithm for Mixture Models.
Neural Computation, 2000

Variational Learning for Switching State-Space Models.
Neural Computation, 2000

Occam's Razor.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000

Propagation Algorithms for Variational Bayesian Learning.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000

MFDTs: Mean Field Dynamic Trees.
Proceedings of the 15th International Conference on Pattern Recognition, 2000

1999
A Unifying Review of Linear Gaussian Models.
Neural Computation, 1999

An Introduction to Variational Methods for Graphical Models.
Machine Learning, 1999

Learning to Parse Images.
Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

Variational Inference for Bayesian Mixtures of Factor Analysers.
Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

1998
SMEM Algorithm for Mixture Models.
Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998

Learning Nonlinear Dynamical Systems Using an EM Algorithm.
Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998

1997
Factorial Hidden Markov Models.
Machine Learning, 1997

Learning Dynamic Bayesian Networks.
Proceedings of the Adaptive Processing of Sequences and Data Structures, 1997

Hierarchical Non-linear Factor Analysis and Topographic Maps.
Proceedings of the Advances in Neural Information Processing Systems 10, 1997

1996
Active Learning with Statistical Models.
J. Artif. Intell. Res., 1996

Active Learning with Statistical Models
CoRR, 1996

Hidden Markov Decision Trees.
Proceedings of the Advances in Neural Information Processing Systems 9, 1996

1995
Factorial Hidden Markov Models.
Proceedings of the Advances in Neural Information Processing Systems 8, 1995

1994
Forward dynamic models in human motor control: Psychophysical evidence.
Proceedings of the Advances in Neural Information Processing Systems 7, 1994

Computational Structure of coordinate transformations: A generalization study.
Proceedings of the Advances in Neural Information Processing Systems 7, 1994

Factorial Learning and the EM Algorithm.
Proceedings of the Advances in Neural Information Processing Systems 7, 1994

Active Learning with Statistical Models.
Proceedings of the Advances in Neural Information Processing Systems 7, 1994

1993
Supervised learning from incomplete data via an EM approach.
Proceedings of the Advances in Neural Information Processing Systems 6, 1993


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