Michael I. Jordan

According to our database1, Michael I. Jordan authored at least 460 papers between 1989 and 2018.

Collaborative distances:

Awards

ACM Fellow

ACM Fellow 2010, "For contributions to the theory and application of machine learning.".

Timeline

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Bibliography

2018
Covariances, Robustness, and Variational Bayes.
Journal of Machine Learning Research, 2018

Probabilistic Multilevel Clustering via Composite Transportation Distance.
CoRR, 2018

Understanding the Acceleration Phenomenon via High-Resolution Differential Equations.
CoRR, 2018

Rao-Blackwellized Stochastic Gradients for Discrete Distributions.
CoRR, 2018

A Deep Generative Model for Semi-Supervised Classification with Noisy Labels.
CoRR, 2018

L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data.
CoRR, 2018

Is Q-learning Provably Efficient?
CoRR, 2018

Improved Oracle Complexity for Stochastic Compositional Variance Reduced Gradient.
CoRR, 2018

Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data.
CoRR, 2018

Information Constraints on Auto-Encoding Variational Bayes.
CoRR, 2018

Sharp Convergence Rates for Langevin Dynamics in the Nonconvex Setting.
CoRR, 2018

Minimizing Nonconvex Population Risk from Rough Empirical Risk.
CoRR, 2018

Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning.
CoRR, 2018

Averaging Stochastic Gradient Descent on Riemannian Manifolds.
CoRR, 2018

SAFFRON: an adaptive algorithm for online control of the false discovery rate.
CoRR, 2018

Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification.
CoRR, 2018

Learning to Explain: An Information-Theoretic Perspective on Model Interpretation.
CoRR, 2018

On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo.
CoRR, 2018

Ray: A Distributed Framework for Emerging AI Applications.
Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation, 2018

SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate.
Proceedings of the 35th International Conference on Machine Learning, 2018

RLlib: Abstractions for Distributed Reinforcement Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018

Learning to Explain: An Information-Theoretic Perspective on Model Interpretation.
Proceedings of the 35th International Conference on Machine Learning, 2018

On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo.
Proceedings of the 35th International Conference on Machine Learning, 2018

Averaging Stochastic Gradient Descent on Riemannian Manifolds.
Proceedings of the Conference On Learning Theory, 2018

Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification.
Proceedings of the Conference On Learning Theory, 2018

Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent.
Proceedings of the Conference On Learning Theory, 2018

Underdamped Langevin MCMC: A non-asymptotic analysis.
Proceedings of the Conference On Learning Theory, 2018

Detection limits in the high-dimensional spiked rectangular model.
Proceedings of the Conference On Learning Theory, 2018

2017
A Marked Poisson Process Driven Latent Shape Model for 3D Segmentation of Reflectance Confocal Microscopy Image Stacks of Human Skin.
IEEE Trans. Image Processing, 2017

Perturbed Iterate Analysis for Asynchronous Stochastic Optimization.
SIAM Journal on Optimization, 2017

Distributed optimization with arbitrary local solvers.
Optimization Methods and Software, 2017

CoCoA: A General Framework for Communication-Efficient Distributed Optimization.
Journal of Machine Learning Research, 2017

Saturating Splines and Feature Selection.
Journal of Machine Learning Research, 2017

Ray: A Distributed Framework for Emerging AI Applications.
CoRR, 2017

A Berkeley View of Systems Challenges for AI.
CoRR, 2017

Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent.
CoRR, 2017

Stochastic Cubic Regularization for Fast Nonconvex Optimization.
CoRR, 2017

First-order Methods Almost Always Avoid Saddle Points.
CoRR, 2017

Finite Size Corrections and Likelihood Ratio Fluctuations in the Spiked Wigner Model.
CoRR, 2017

Online control of the false discovery rate with decaying memory.
CoRR, 2017

DAGGER: A sequential algorithm for FDR control on DAGs.
CoRR, 2017

A deep generative model for gene expression profiles from single-cell RNA sequencing.
CoRR, 2017

Fast Black-box Variational Inference through Stochastic Trust-Region Optimization.
CoRR, 2017

Real-Time Machine Learning: The Missing Pieces.
CoRR, 2017

Domain Adaptation with Randomized Multilinear Adversarial Networks.
CoRR, 2017

Nonconvex Finite-Sum Optimization Via SCSG Methods.
CoRR, 2017

How to Escape Saddle Points Efficiently.
CoRR, 2017

Gradient Descent Can Take Exponential Time to Escape Saddle Points.
CoRR, 2017

Underdamped Langevin MCMC: A non-asymptotic analysis.
CoRR, 2017

Kernel Feature Selection via Conditional Covariance Minimization.
CoRR, 2017

Partial Transfer Learning with Selective Adversarial Networks.
CoRR, 2017

Decoding from Pooled Data: Phase Transitions of Message Passing.
CoRR, 2017

On Gradient-Based Optimization: Accelerated, Distributed, Asynchronous and Stochastic.
Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, Urbana-Champaign, IL, USA, June 05, 2017

Fast Black-box Variational Inference through Stochastic Trust-Region Optimization.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Online control of the false discovery rate with decaying memory.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Non-convex Finite-Sum Optimization Via SCSG Methods.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Gradient Descent Can Take Exponential Time to Escape Saddle Points.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Kernel Feature Selection via Conditional Covariance Minimization.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Decoding from pooled data: Phase transitions of message passing.
Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017

Breaking Locality Accelerates Block Gauss-Seidel.
Proceedings of the 34th International Conference on Machine Learning, 2017

Deep Transfer Learning with Joint Adaptation Networks.
Proceedings of the 34th International Conference on Machine Learning, 2017

How to Escape Saddle Points Efficiently.
Proceedings of the 34th International Conference on Machine Learning, 2017

Real-Time Machine Learning: The Missing Pieces.
Proceedings of the 16th Workshop on Hot Topics in Operating Systems, 2017

QuTE: Decentralized multiple testing on sensor networks with false discovery rate control.
Proceedings of the 56th IEEE Annual Conference on Decision and Control, 2017

On the Learnability of Fully-Connected Neural Networks.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

Less than a Single Pass: Stochastically Controlled Stochastic Gradient.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing.
Journal of Machine Learning Research, 2016

A Lyapunov Analysis of Momentum Methods in Optimization.
CoRR, 2016

A Variational Perspective on Accelerated Methods in Optimization.
CoRR, 2016

CoCoA: A General Framework for Communication-Efficient Distributed Optimization.
CoRR, 2016

Function-Specific Mixing Times and Concentration Away from Equilibrium.
CoRR, 2016

CYCLADES: Conflict-free Asynchronous Machine Learning.
CoRR, 2016

Universality of Mallows' and degeneracy of Kendall's kernels for rankings.
CoRR, 2016

Deep Transfer Learning with Joint Adaptation Networks.
CoRR, 2016

Unsupervised Domain Adaptation with Residual Transfer Networks.
CoRR, 2016

Less than a Single Pass: Stochastically Controlled Stochastic Gradient Method.
CoRR, 2016

Gradient Descent Converges to Minimizers.
CoRR, 2016

Communication-efficient distributed statistical learning.
CoRR, 2016

Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences.
CoRR, 2016

Minimax Optimal Procedures for Locally Private Estimation.
CoRR, 2016

Decoding from Pooled Data: Sharp Information-Theoretic Bounds.
CoRR, 2016

Asymptotic behavior of ℓp-based Laplacian regularization in semi-supervised learning.
CoRR, 2016

On Computational Thinking, Inferential Thinking and Data Science.
Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures, 2016

Unsupervised Domain Adaptation with Residual Transfer Networks.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

L1-regularized Neural Networks are Improperly Learnable in Polynomial Time.
Proceedings of the 33nd International Conference on Machine Learning, 2016

A Kernelized Stein Discrepancy for Goodness-of-fit Tests.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Gradient Descent Only Converges to Minimizers.
Proceedings of the 29th Conference on Learning Theory, 2016

A Linearly-Convergent Stochastic L-BFGS Algorithm.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

The Constrained Laplacian Rank Algorithm for Graph-Based Clustering.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations.
IEEE Trans. Information Theory, 2015

Nested Hierarchical Dirichlet Processes.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Combinatorial Clustering and the Beta Negative Binomial Process.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Distributed matrix completion and robust factorization.
Journal of Machine Learning Research, 2015

Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds.
CoRR, 2015

Learning Halfspaces and Neural Networks with Random Initialization.
CoRR, 2015

1-regularized Neural Networks are Improperly Learnable in Polynomial Time.
CoRR, 2015

Splash: User-friendly Programming Interface for Parallelizing Stochastic Algorithms.
CoRR, 2015

On the Computational Complexity of High-Dimensional Bayesian Variable Selection.
CoRR, 2015

TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries.
CoRR, 2015

L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework.
CoRR, 2015

High-Dimensional Continuous Control Using Generalized Advantage Estimation.
CoRR, 2015

Trust Region Policy Optimization.
CoRR, 2015

Parallel Correlation Clustering on Big Graphs.
CoRR, 2015

A General Analysis of the Convergence of ADMM.
CoRR, 2015

SparkNet: Training Deep Networks in Spark.
CoRR, 2015

A Linearly-Convergent Stochastic L-BFGS Algorithm.
CoRR, 2015

Perturbed Iterate Analysis for Asynchronous Stochastic Optimization.
CoRR, 2015

Adding vs. Averaging in Distributed Primal-Dual Optimization.
CoRR, 2015

Distributed Optimization with Arbitrary Local Solvers.
CoRR, 2015

Asynchronous Complex Analytics in a Distributed Dataflow Architecture.
CoRR, 2015

On the accuracy of self-normalized log-linear models.
CoRR, 2015

Machine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype?
Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia, May 31, 2015

Computational Thinking, Inferential Thinking and "Big Data".
Proceedings of the 34th ACM Symposium on Principles of Database Systems, 2015

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

Parallel Correlation Clustering on Big Graphs.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

On the Accuracy of Self-Normalized Log-Linear Models.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Optimism-driven exploration for nonlinear systems.
Proceedings of the IEEE International Conference on Robotics and Automation, 2015

Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Trust Region Policy Optimization.
Proceedings of the 32nd International Conference on Machine Learning, 2015

A General Analysis of the Convergence of ADMM.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Adding vs. Averaging in Distributed Primal-Dual Optimization.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Learning Transferable Features with Deep Adaptation Networks.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Automating model search for large scale machine learning.
Proceedings of the Sixth ACM Symposium on Cloud Computing, 2015

The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox.
Proceedings of the CIDR 2015, 2015

2014
Bayesian Nonnegative Matrix Factorization with Stochastic Variational Inference.
Proceedings of the Handbook of Mixed Membership Models and Their Applications., 2014

Mixed Membership Matrix Factorization.
Proceedings of the Handbook of Mixed Membership Models and Their Applications., 2014

Mixed Membership Models for Time Series.
Proceedings of the Handbook of Mixed Membership Models and Their Applications., 2014

Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning.
PVLDB, 2014

Iterative Discovery of Multiple AlternativeClustering Views.
IEEE Trans. Pattern Anal. Mach. Intell., 2014

Particle gibbs with ancestor sampling.
Journal of Machine Learning Research, 2014

Privacy Aware Learning.
J. ACM, 2014

Lower bounds on the performance of polynomial-time algorithms for sparse linear regression.
CoRR, 2014

On the Convergence Rate of Decomposable Submodular Function Minimization.
CoRR, 2014

Communication-Efficient Distributed Dual Coordinate Ascent.
CoRR, 2014

Information-theoretic lower bounds for distributed statistical estimation with communication constraints.
CoRR, 2014

The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox.
CoRR, 2014

SMaSH: a benchmarking toolkit for human genome variant calling.
Bioinformatics, 2014

Knowing when you're wrong: building fast and reliable approximate query processing systems.
Proceedings of the International Conference on Management of Data, 2014

Changepoint Analysis for Efficient Variant Calling.
Proceedings of the Research in Computational Molecular Biology, 2014

Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Parallel Double Greedy Submodular Maximization.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

On the Convergence Rate of Decomposable Submodular Function Minimization.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Communication-Efficient Distributed Dual Coordinate Ascent.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Lower bounds on the performance of polynomial-time algorithms for sparse linear regression.
Proceedings of The 27th Conference on Learning Theory, 2014

Neural Networks.
Proceedings of the Computing Handbook, 2014

2013
Cluster Forests.
Computational Statistics & Data Analysis, 2013

Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation
CoRR, 2013

Divide-and-Conquer Subspace Segmentation
CoRR, 2013

Computing Upper and Lower Bounds on Likelihoods in Intractable Networks
CoRR, 2013

Local Privacy and Statistical Minimax Rates
CoRR, 2013

Mixture Representations for Inference and Learning in Boltzmann Machines
CoRR, 2013

Loopy Belief Propagation for Approximate Inference: An Empirical Study
CoRR, 2013

PEGASUS: A Policy Search Method for Large MDPs and POMDPs
CoRR, 2013

Efficient Stepwise Selection in Decomposable Models
CoRR, 2013

Variational MCMC
CoRR, 2013

Loopy Belief Propogation and Gibbs Measures
CoRR, 2013

Tree-dependent Component Analysis
CoRR, 2013

MLI: An API for Distributed Machine Learning.
CoRR, 2013

Optimistic Concurrency Control for Distributed Unsupervised Learning.
CoRR, 2013

On statistics, computation and scalability.
CoRR, 2013

Mixed Membership Models for Time Series.
CoRR, 2013

Optimal rates for zero-order optimization: the power of two function evaluations.
CoRR, 2013

Streaming Variational Bayes.
CoRR, 2013

Learning Dependency-Based Compositional Semantics.
Computational Linguistics, 2013

Bayesian semiparametric Wiener system identification.
Automatica, 2013

Information-theoretic lower bounds for distributed statistical estimation with communication constraints.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

A Comparative Framework for Preconditioned Lasso Algorithms.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Optimistic Concurrency Control for Distributed Unsupervised Learning.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Estimation, Optimization, and Parallelism when Data is Sparse.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Streaming Variational Bayes.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

A general bootstrap performance diagnostic.
Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

Efficient Ranking from Pairwise Comparisons.
Proceedings of the 30th International Conference on Machine Learning, 2013

MAD-Bayes: MAP-based Asymptotic Derivations from Bayes.
Proceedings of the 30th International Conference on Machine Learning, 2013

MLI: An API for Distributed Machine Learning.
Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

Distributed Low-Rank Subspace Segmentation.
Proceedings of the IEEE International Conference on Computer Vision, 2013

Local Privacy and Statistical Minimax Rates.
Proceedings of the 54th Annual IEEE Symposium on Foundations of Computer Science, 2013

MLbase: A Distributed Machine-learning System.
Proceedings of the CIDR 2013, 2013

Local privacy and statistical minimax rates.
Proceedings of the 51st Annual Allerton Conference on Communication, 2013

2012
Ergodic Mirror Descent.
SIAM Journal on Optimization, 2012

Qualcomm Context-Awareness Symposium Sets Research Agenda for Context-Aware Smartphones.
IEEE Pervasive Computing, 2012

EP-GIG Priors and Applications in Bayesian Sparse Learning.
Journal of Machine Learning Research, 2012

Coherence functions with applications in large-margin classification methods.
Journal of Machine Learning Research, 2012

Stick-Breaking Beta Processes and the Poisson Process.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

A Generalized Mean Field Algorithm for Variational Inference in Exponential Families
CoRR, 2012

Computational and Statistical Tradeoffs via Convex Relaxation
CoRR, 2012

Nested Hierarchical Dirichlet Processes
CoRR, 2012

Privacy Aware Learning
CoRR, 2012

Active Learning for Crowd-Sourced Databases
CoRR, 2012

Graph partition strategies for generalized mean field inference
CoRR, 2012

The DLR Hierarchy of Approximate Inference
CoRR, 2012

The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features
CoRR, 2012

Optimization of Structured Mean Field Objectives
CoRR, 2012

The Asymptotics of Ranking Algorithms
CoRR, 2012

Modeling Events with Cascades of Poisson Processes
CoRR, 2012

Bayesian Multicategory Support Vector Machines.
CoRR, 2012

Ancestor Sampling for Particle Gibbs.
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

Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models.
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

Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods.
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

Privacy Aware 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

Active spectral clustering via iterative uncertainty reduction.
Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012

Divide-and-conquer and statistical inference for big data.
Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012

Nonparametric Link Prediction in Dynamic Networks.
Proceedings of the 29th International Conference on Machine Learning, 2012

Variational Bayesian Inference with Stochastic Search.
Proceedings of the 29th International Conference on Machine Learning, 2012

Revisiting k-means: New Algorithms via Bayesian Nonparametrics.
Proceedings of the 29th International Conference on Machine Learning, 2012

The Big Data Bootstrap.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Bayesian Nonparametric Inference of Switching Dynamic Linear Models.
IEEE Trans. Signal Processing, 2011

Learning Low-Dimensional Signal Models.
IEEE Signal Process. Mag., 2011

Bayesian Generalized Kernel Mixed Models.
Journal of Machine Learning Research, 2011

Dimensionality Reduction for Spectral Clustering.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Nonparametric Combinatorial Sequence Models.
Journal of Computational Biology, 2011

Revisiting k-means: New Algorithms via Bayesian Nonparametrics
CoRR, 2011

Learning Dependency-Based Compositional Semantics
CoRR, 2011

Non-parametric Link Prediction
CoRR, 2011

Divide-and-Conquer Matrix Factorization
CoRR, 2011

Variational Probabilistic Inference and the QMR-DT Network
CoRR, 2011

Cluster Forests
CoRR, 2011

Managing data transfers in computer clusters with orchestra.
Proceedings of the ACM SIGCOMM 2011 Conference on Applications, 2011

Nonparametric Bayesian Co-clustering Ensembles.
Proceedings of the Eleventh SIAM International Conference on Data Mining, 2011

Nonparametric Combinatorial Sequence Models.
Proceedings of the Research in Computational Molecular Biology, 2011

Bayesian Bias Mitigation for Crowdsourcing.
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

Divide-and-Conquer Matrix Factorization.
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

A Unified Probabilistic Model for Global and Local Unsupervised Feature Selection.
Proceedings of the 28th International Conference on Machine Learning, 2011

The SCADS Director: Scaling a Distributed Storage System Under Stringent Performance Requirements.
Proceedings of the 9th USENIX Conference on File and Storage Technologies, 2011

Supervised hierarchical Pitman-Yor process for natural scene segmentation.
Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition, 2011

Visually Relating Gene Expression and in vivo DNA Binding Data.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2011

Ergodic mirror descent.
Proceedings of the 49th Annual Allerton Conference on Communication, 2011

Learning Dependency-Based Compositional Semantics.
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011

2010
Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization.
IEEE Trans. Information Theory, 2010

Bayesian Nonparametric Methods for Learning Markov Switching Processes.
IEEE Signal Process. Mag., 2010

Joint covariate selection and joint subspace selection for multiple classification problems.
Statistics and Computing, 2010

Neighbor-Dependent Ramachandran Probability Distributions of Amino Acids Developed from a Hierarchical Dirichlet Process Model.
PLoS Computational Biology, 2010

Convex and Semi-Nonnegative Matrix Factorizations.
IEEE Trans. Pattern Anal. Mach. Intell., 2010

Regularized Discriminant Analysis, Ridge Regression and Beyond.
Journal of Machine Learning Research, 2010

Bayesian Generalized Kernel Models.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Matrix-Variate Dirichlet Process Mixture Models.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Inference and Learning in Networks of Queues.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies.
J. ACM, 2010

Bayesian Inference in Queueing Networks
CoRR, 2010

Active site prediction using evolutionary and structural information.
Bioinformatics, 2010

Modeling Events with Cascades of Poisson Processes.
Proceedings of the UAI 2010, 2010

Experience Mining Google's Production Console Logs.
Proceedings of the Workshop on Managing Systems via Log Analysis and Machine Learning Techniques, 2010

Heavy-Tailed Process Priors for Selective Shrinkage.
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

Unsupervised Kernel Dimension Reduction.
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

Random Conic Pursuit for Semidefinite Programming.
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

Variational Inference over Combinatorial Spaces.
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

Type-Based MCMC.
Proceedings of the Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, 2010

Detecting Large-Scale System Problems by Mining Console Logs.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

An Analysis of the Convergence of Graph Laplacians.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Multiple Non-Redundant Spectral Clustering Views.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Mixed Membership Matrix Factorization.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Learning Programs: A Hierarchical Bayesian Approach.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

On the Consistency of Ranking Algorithms.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Sufficient dimension reduction for visual sequence classification.
Proceedings of the Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, 2010

Characterizing, modeling, and generating workload spikes for stateful services.
Proceedings of the 1st ACM Symposium on Cloud Computing, 2010

2009
Coherence Functions for Multicategory Margin-based Classification Methods.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Latent Variable Models for Dimensionality Reduction.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Joint estimation of gene conversion rates and mean conversion tract lengths from population SNP data.
Bioinformatics, 2009

Optimization of Structured Mean Field Objectives.
Proceedings of the UAI 2009, 2009

Detecting large-scale system problems by mining console logs.
Proceedings of the 22nd ACM Symposium on Operating Systems Principles 2009, 2009

Combinatorial stochastic processes and nonparametric Bayesian modeling.
Proceedings of the Twentieth Annual ACM-SIAM Symposium on Discrete Algorithms, 2009

A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2009

Nonparametric Latent Feature Models for Link Prediction.
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

Asymptotically Optimal Regularization in Smooth Parametric Models.
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

Sharing Features among Dynamical Systems with Beta Processes.
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

Fast approximate spectral clustering.
Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28, 2009

Learning from measurements in exponential families.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Online System Problem Detection by Mining Patterns of Console Logs.
Proceedings of the ICDM 2009, 2009

Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning.
Proceedings of the 25th International Conference on Data Engineering, 2009

Statistical Machine Learning Makes Automatic Control Practical for Internet Datacenters.
Proceedings of the Workshop on Hot Topics in Cloud Computing, 2009

Learning Semantic Correspondences with Less Supervision.
Proceedings of the ACL 2009, 2009

2008
On Optimal Quantization Rules for Some Problems in Sequential Decentralized Detection.
IEEE Trans. Information Theory, 2008

A Dual Receptor Crosstalk Model of G-Protein-Coupled Signal Transduction.
PLoS Computational Biology, 2008

Graphical Models, Exponential Families, and Variational Inference.
Foundations and Trends in Machine Learning, 2008

Estimating divergence functionals and the likelihood ratio by convex risk minimization
CoRR, 2008

The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features.
Proceedings of the UAI 2008, 2008

On the Inference of Ancestries in Admixed Populations.
Proceedings of the Research in Computational Molecular Biology, 2008

Mining Console Logs for Large-Scale System Problem Detection.
Proceedings of the Third Workshop on Tackling Computer Systems Problems with Machine Learning Techniques, 2008

Probabilistic Inference in Queueing Networks.
Proceedings of the Third Workshop on Tackling Computer Systems Problems with Machine Learning Techniques, 2008

Posterior Consistency of the Silverman g-prior in Bayesian Model Choice.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

High-dimensional support union recovery in multivariate regression.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Spectral Clustering with Perturbed Data.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Nonparametric Bayesian Learning of Switching Linear Dynamical Systems.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Efficient Inference in Phylogenetic InDel Trees.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators.
Proceedings of the Machine Learning, 2008

An HDP-HMM for systems with state persistence.
Proceedings of the Machine Learning, 2008

Nonnegative Matrix Factorization for Combinatorial Optimization: Spectral Clustering, Graph Matching, and Clique Finding.
Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 2008

2007
A Direct Formulation for Sparse PCA Using Semidefinite Programming.
SIAM Review, 2007

Hierarchical Beta Processes and the Indian Buffet Process.
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Bayesian Haplotype Inference via the Dirichlet Process.
Journal of Computational Biology, 2007

Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Agreement-Based Learning.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Feature Selection Methods for Improving Protein Structure Prediction with Rosetta.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Nonparametric estimation of the likelihood ratio and divergence functionals.
Proceedings of the IEEE International Symposium on Information Theory, 2007

Communication-Efficient Online Detection of Network-Wide Anomalies.
Proceedings of the INFOCOM 2007. 26th IEEE International Conference on Computer Communications, 2007

Regression on manifolds using kernel dimension reduction.
Proceedings of the Machine Learning, 2007

A permutation-augmented sampler for DP mixture models.
Proceedings of the Machine Learning, 2007

Image Denoising with Nonparametric Hidden Markov Trees.
Proceedings of the International Conference on Image Processing, 2007

Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization.
Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), 2007

Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes.
Proceedings of the IEEE 11th International Conference on Computer Vision, 2007

The Infinite PCFG Using Hierarchical Dirichlet Processes.
Proceedings of the EMNLP-CoNLL 2007, 2007

Statistical Machine Learning and Computational Biology.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2007

2006
Log-determinant relaxation for approximate inference in discrete Markov random fields.
IEEE Trans. Signal Processing, 2006

Nonparametric empirical Bayes for the Dirichlet process mixture model.
Statistics and Computing, 2006

Structured Prediction, Dual Extragradient and Bregman Projections.
Journal of Machine Learning Research, 2006

Learning Spectral Clustering, With Application To Speech Separation.
Journal of Machine Learning Research, 2006

On optimal quantization rules for some sequential decision problems
CoRR, 2006

Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span.
BMC Bioinformatics, 2006

Bayesian Multicategory Support Vector Machines.
Proceedings of the UAI '06, 2006

In-Network PCA and Anomaly Detection.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Word Alignment via Quadratic Assignment.
Proceedings of the Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, 2006

Statistical debugging: simultaneous identification of multiple bugs.
Proceedings of the Machine Learning, 2006

Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture.
Proceedings of the Machine Learning, 2006

A graphical model for predicting protein molecular function.
Proceedings of the Machine Learning, 2006

2005
A kernel-based learning approach to ad hoc sensor network localization.
TOSN, 2005

Protein Molecular Function Prediction by Bayesian Phylogenomics.
PLoS Computational Biology, 2005

On divergences, surrogate loss functions, and decentralized detection
CoRR, 2005

A latent variable model for chemogenomic profiling.
Bioinformatics, 2005

The DLR Hierarchy of Approximate Inference.
Proceedings of the UAI '05, 2005

Scalable statistical bug isolation.
Proceedings of the ACM SIGPLAN 2005 Conference on Programming Language Design and Implementation, 2005

Structured Prediction via the Extragradient Method.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Divergences, surrogate loss functions and experimental design.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Robust design of biological experiments.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Predictive low-rank decomposition for kernel methods.
Proceedings of the Machine Learning, 2005

Multi-instrument musical transcription using a dynamic graphical model.
Proceedings of the 2005 IEEE International Conference on Acoustics, 2005

Discriminative training of hidden Markov models for multiple pitch tracking [speech processing examples].
Proceedings of the 2005 IEEE International Conference on Acoustics, 2005

Combining Visualization and Statistical Analysis to Improve Operator Confidence and Efficiency for Failure Detection and Localization.
Proceedings of the Second International Conference on Autonomic Computing (ICAC 2005), 2005

Modèles de Markov cachés pour l'estimation de plusieurs fréquences fondamentales.
Proceedings of the Extraction des connaissances : Etat et perspectives (Ateliers de la conférence EGC'2005), 2005

Semiparametric latent factor models.
Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005

2004
Learning graphical models for stationary time series.
IEEE Trans. Signal Processing, 2004

Kalman filtering with intermittent observations.
IEEE Trans. Automat. Contr., 2004

Learning the Kernel Matrix with Semidefinite Programming.
Journal of Machine Learning Research, 2004

Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces.
Journal of Machine Learning Research, 2004

Robust Sparse Hyperplane Classifiers: Application to Uncertain Molecular Profiling Data.
Journal of Computational Biology, 2004

Logos: a Modular Bayesian Model for de Novo Motif Detection.
J. Bioinformatics and Computational Biology, 2004

A direct formulation for sparse PCA using semidefinite programming
CoRR, 2004

Multiple-sequence functional annotation and the generalized hidden Markov phylogeny.
Bioinformatics, 2004

A statistical framework for genomic data fusion.
Bioinformatics, 2004

Graph Partition Strategies for Generalized Mean Field Inference.
Proceedings of the UAI '04, 2004

Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast.
Proceedings of the Biocomputing 2004, 2004

A Direct Formulation for Sparse PCA Using Semidefinite Programming.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Semi-supervised Learning via Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Computing regularization paths for learning multiple kernels.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Blind One-microphone Speech Separation: A Spectral Learning Approach.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Bayesian haplo-type inference via the dirichlet process.
Proceedings of the Machine Learning, 2004

Decentralized detection and classification using kernel methods.
Proceedings of the Machine Learning, 2004

Variational methods for the Dirichlet process.
Proceedings of the Machine Learning, 2004

Multiple kernel learning, conic duality, and the SMO algorithm.
Proceedings of the Machine Learning, 2004

Failure Diagnosis Using Decision Trees.
Proceedings of the 1st International Conference on Autonomic Computing (ICAC 2004), 2004

Extensions of the Informative Vector Machine.
Proceedings of the Deterministic and Statistical Methods in Machine Learning, 2004

2003
Simultaneous classification and relevant feature identification in high-dimensional spaces: application to molecular profiling data.
Signal Processing, 2003

An Introduction to MCMC for Machine Learning.
Machine Learning, 2003

Latent Dirichlet Allocation.
Journal of Machine Learning Research, 2003

Matching Words and Pictures.
Journal of Machine Learning Research, 2003

Beyond Independent Components: Trees and Clusters.
Journal of Machine Learning Research, 2003

A generalized mean field algorithm for variational inference in exponential families.
Proceedings of the UAI '03, 2003

Modeling annotated data.
Proceedings of the SIGIR 2003: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 28, 2003

Bug isolation via remote program sampling.
Proceedings of the ACM SIGPLAN 2003 Conference on Programming Language Design and Implementation 2003, 2003

Statistical Debugging of Sampled Programs.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Semidefinite Relaxations for Approximate Inference on Graphs with Cycles.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

On the Concentration of Expectation and Approximate Inference in Layered Networks.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Autonomous Helicopter Flight via Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Kernel Dimensionality Reduction for Supervised Learning.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Hierarchical Topic Models and the Nested Chinese Restaurant Process.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Learning Spectral Clustering.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Kernel independent component analysis.
Proceedings of the 2003 IEEE International Conference on Acoustics, 2003

Support vector machines for analog circuit performance representation.
Proceedings of the 40th Design Automation Conference, 2003

LOGOS: a modular Bayesian model for de novo motif detection.
Proceedings of the 2nd IEEE Computer Society Bioinformatics Conference, 2003

2002
Graphical Models: Foundations of Neural Computation.
Pattern Anal. Appl., 2002

A Robust Minimax Approach to Classification.
Journal of Machine Learning Research, 2002

Kernel Independent Component Analysis.
Journal of Machine Learning Research, 2002

Simultaneous Relevant Feature Identification and Classification in High-Dimensional Spaces.
Proceedings of the Algorithms in Bioinformatics, Second International Workshop, 2002

Loopy Belief Propogation and Gibbs Measures.
Proceedings of the UAI '02, 2002

Tree-dependent Component Analysis.
Proceedings of the UAI '02, 2002

Distance Metric Learning with Application to Clustering with Side-Information.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

A Minimal Intervention Principle for Coordinated Movement.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Robust Novelty Detection with Single-Class MPM.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Learning Graphical Models with Mercer Kernels.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Learning the Kernel Matrix with Semi-Definite Programming.
Proceedings of the Machine Learning, 2002

2001
Asymptotic Convergence Rate of the EM Algorithm for Gaussian Mixtures.
Neural Computation, 2001

Efficient Stepwise Selection in Decomposable Models.
Proceedings of the UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 2001

Stable Algorithms for Link Analysis.
Proceedings of the SIGIR 2001: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2001

On Spectral Clustering: Analysis and an algorithm.
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes.
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

Minimax Probability Machine.
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

Latent Dirichlet Allocation.
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

Thin Junction Trees.
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

Link Analysis, Eigenvectors and Stability.
Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, 2001

Feature selection for high-dimensional genomic microarray data.
Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28, 2001

Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection.
Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28, 2001

2000
Bayesian parameter estimation via variational methods.
Statistics and Computing, 2000

Attractor Dynamics in Feedforward Neural Networks.
Neural Computation, 2000

Learning with Mixtures of Trees.
Journal of Machine Learning Research, 2000

PEGASUS: A policy search method for large MDPs and POMDPs.
Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000

1999
Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones.
Machine Learning, 1999

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

Variational Probabilistic Inference and the QMR-DT Network.
J. Artif. Intell. Res., 1999

Loopy Belief Propagation for Approximate Inference: An Empirical Study.
Proceedings of the UAI '99: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, July 30, 1999

Approximate Inference A lgorithms for Two-Layer Bayesian Networks.
Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

1998
Mixture Representations for Inference and Learning in Boltzmann Machines.
Proceedings of the UAI '98: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 1998

Learning from Dyadic Data.
Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998

1997
Probabilistic Independence Networks for Hidden Markov Probability Models.
Neural Computation, 1997

Factorial Hidden Markov Models.
Machine Learning, 1997

Estimating Dependency Structure as a Hidden Variable.
Proceedings of the Advances in Neural Information Processing Systems 10, 1997

Adaptation in Speech Motor Control.
Proceedings of the Advances in Neural Information Processing Systems 10, 1997

Approximating Posterior Distributions in Belief Networks Using Mixtures.
Proceedings of the Advances in Neural Information Processing Systems 10, 1997

Neural Networks.
Proceedings of the Computer Science and Engineering Handbook, 1997

1996
Local linear perceptrons for classification.
IEEE Trans. Neural Networks, 1996

Mean Field Theory for Sigmoid Belief Networks.
J. Artif. Intell. Res., 1996

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

Neural Networks.
ACM Comput. Surv., 1996

Active Learning with Statistical Models
CoRR, 1996

Mean Field Theory for Sigmoid Belief Networks
CoRR, 1996

Computing upper and lower bounds on likelihoods in intractable networks.
Proceedings of the UAI '96: Proceedings of the Twelfth Annual Conference on Uncertainty in Artificial Intelligence, 1996

A Variational Principle for Model-based Morphing.
Proceedings of the Advances in Neural Information Processing Systems 9, 1996

Triangulation by Continuous Embedding.
Proceedings of the Advances in Neural Information Processing Systems 9, 1996

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

Recursive Algorithms for Approximating Probabilities in Graphical Models.
Proceedings of the Advances in Neural Information Processing Systems 9, 1996

1995
Convergence results for the EM approach to mixtures of experts architectures.
Neural Networks, 1995

Exploiting Tractable Substructures in Intractable Networks.
Proceedings of the Advances in Neural Information Processing Systems 8, 1995

Reinforcement Learning by Probability Matching.
Proceedings of the Advances in Neural Information Processing Systems 8, 1995

Learning Fine Motion by Markov Mixtures of Experts.
Proceedings of the Advances in Neural Information Processing Systems 8, 1995

Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks.
Proceedings of the Advances in Neural Information Processing Systems 8, 1995

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

1994
Learning in Boltzmann Trees.
Neural Computation, 1994

Hierarchical Mixtures of Experts and the EM Algorithm.
Neural Computation, 1994

On the Convergence of Stochastic Iterative Dynamic Programming Algorithms.
Neural Computation, 1994

An Alternative Model for Mixtures of Experts.
Proceedings of the Advances in Neural Information Processing Systems 7, 1994

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

Reinforcement Learning with Soft State Aggregation.
Proceedings of the Advances in Neural Information Processing Systems 7, 1994

Boltzmann Chains and Hidden Markov Models.
Proceedings of the Advances in Neural Information Processing Systems 7, 1994

Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems.
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

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

Learning Without State-Estimation in Partially Observable Markovian Decision Processes.
Proceedings of the Machine Learning, 1994

A Statistical Approach to Decision Tree Modeling.
Proceedings of the Machine Learning, 1994

A Statistical Approach to Decision Tree Modeling.
Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory, 1994

1993
Learning piecewise control strategies in a modular neural network architecture.
IEEE Trans. Systems, Man, and Cybernetics, 1993

Task Decompostiion Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks.
Proceedings of the Machine Learning: From Theory to Applications, 1993

Convergence of Stochastic Iterative Dynamic Programming Algorithms.
Proceedings of the Advances in Neural Information Processing Systems 6, 1993

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

Supervised Learning and Divide-and-Conquer: A Statistical Approach.
Proceedings of the Machine Learning, 1993

1992
Forward Models: Supervised Learning with a Distal Teacher.
Cognitive Science, 1992

A Dynamical Model of Priming and Repetition Blindness.
Proceedings of the Advances in Neural Information Processing Systems 5, [NIPS Conference, Denver, Colorado, USA, November 30, 1992

1991
Adaptive Mixtures of Local Experts.
Neural Computation, 1991

Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks.
Cognitive Science, 1991

Hierarchies of Adaptive Experts.
Proceedings of the Advances in Neural Information Processing Systems 4, 1991

Forward Dynamics Modeling of Speech Motor Control Using Physiological Data.
Proceedings of the Advances in Neural Information Processing Systems 4, 1991

Internal World Models and Supervised Learning.
Proceedings of the Eighth International Workshop (ML91), 1991

1990
A Competitive Modular Connectionist Architecture.
Proceedings of the Advances in Neural Information Processing Systems 3, 1990

A R-P learning applied to a network model of cortical area 7a.
Proceedings of the IJCNN 1990, 1990

1989
Learning to Control an Unstable System with Forward Modeling.
Proceedings of the Advances in Neural Information Processing Systems 2, 1989


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