NIPS Proceedingsβ

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)