NIPS Proceedings^β

Michael I. Jordan

79 Papers

Ancestor Sampling for Particle Gibbs (2012)

Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods (2012)

Privacy Aware Learning (2012)

Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models (2012)

Bayesian Bias Mitigation for Crowdsourcing (2011)

Divide-and-Conquer Matrix Factorization (2011)

Heavy-Tailed Process Priors for Selective Shrinkage (2010)

Random Conic Pursuit for Semidefinite Programming (2010)

Tree-Structured Stick Breaking for Hierarchical Data (2010)

Unsupervised Kernel Dimension Reduction (2010)

Variational Inference over Combinatorial Spaces (2010)

Asymptotically Optimal Regularization in Smooth Parametric Models (2009)

Nonparametric Latent Feature Models for Link Prediction (2009)

Sharing Features among Dynamical Systems with Beta Processes (2009)

DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification (2008)

Efficient Inference in Phylogenetic InDel Trees (2008)

High-dimensional support union recovery in multivariate regression (2008)

Nonparametric Bayesian Learning of Switching Linear Dynamical Systems (2008)

Posterior Consistency of the Silverman g-prior in Bayesian Model Choice (2008)

Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes (2008)

Spectral Clustering with Perturbed Data (2008)

Agreement-Based Learning (2007)

Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization (2007)

Feature Selection Methods for Improving Protein Structure Prediction with Rosetta (2007)

In-Network PCA and Anomaly Detection (2006)

Divergences, surrogate loss functions and experimental design (2005)

Robust design of biological experiments (2005)

Structured Prediction via the Extragradient Method (2005)

A Direct Formulation for Sparse PCA Using Semidefinite Programming (2004)

Blind One-microphone Speech Separation: A Spectral Learning Approach (2004)

Computing regularization paths for learning multiple kernels (2004)

Semi-supervised Learning via Gaussian Processes (2004)

Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes (2004)

Autonomous Helicopter Flight via Reinforcement Learning (2003)

Hierarchical Topic Models and the Nested Chinese Restaurant Process (2003)

Kernel Dimensionality Reduction for Supervised Learning (2003)

Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates (2003)

Learning Spectral Clustering (2003)

On the Concentration of Expectation and Approximate Inference in Layered Networks (2003)

Semidefinite Relaxations for Approximate Inference on Graphs with Cycles (2003)

Statistical Debugging of Sampled Programs (2003)

A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences (2002)

A Minimal Intervention Principle for Coordinated Movement (2002)

Distance Metric Learning with Application to Clustering with Side-Information (2002)

Learning Graphical Models with Mercer Kernels (2002)

Robust Novelty Detection with Single-Class MPM (2002)

Latent Dirichlet Allocation (2001)

Minimax Probability Machine (2001)

On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes (2001)

On Spectral Clustering: Analysis and an algorithm (2001)

Thin Junction Trees (2001)

Approximate Inference A lgorithms for Two-Layer Bayesian Networks (1999)

Learning from Dyadic Data (1998)

Adaptation in Speech Motor Control (1997)

Approximating Posterior Distributions in Belief Networks Using Mixtures (1997)

Estimating Dependency Structure as a Hidden Variable (1997)

A Variational Principle for Model-based Morphing (1996)

Hidden Markov Decision Trees (1996)

Recursive Algorithms for Approximating Probabilities in Graphical Models (1996)

Triangulation by Continuous Embedding (1996)

Exploiting Tractable Substructures in Intractable Networks (1995)

Factorial Hidden Markov Models (1995)

Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks (1995)

Learning Fine Motion by Markov Mixtures of Experts (1995)

Reinforcement Learning by Probability Matching (1995)

Active Learning with Statistical Models (1994)

An Alternative Model for Mixtures of Experts (1994)

Boltzmann Chains and Hidden Markov Models (1994)

Computational Structure of coordinate transformations: A generalization study (1994)

Forward dynamic models in human motor control: Psychophysical evidence (1994)

Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems (1994)

Reinforcement Learning with Soft State Aggregation (1994)

Convergence of Stochastic Iterative Dynamic Programming Algorithms (1993)

Supervised learning from incomplete data via an EM approach (1993)

A dynamical model of priming and repetition blindness (1992)

Forward Dynamics Modeling of Speech Motor Control Using Physiological Data (1991)

Hierarchies of adaptive experts (1991)

A competitive modular connectionist architecture (1990)

Learning to Control an Unstable System with Forward Modeling (1989)

2 Books

Advances in Neural Information Processing Systems 10 (1997)

Advances in Neural Information Processing Systems 9 (1996)