WhatToSee

Index of ICML 2009

• A Bayesian Approach to Protein Model Quality Assessment
• A Convex Formulation for Learning Shared Structures from Multiple Tasks
• A Least Squares Formulation for a Class of Generalized Eigenvalue Problems in Machine Learning
• A Novel Lexicalized HMM-based Learning Framework for Web Opinion Mining
• A Scalable Framework for Discovering Coherent Co-clusters in Noisy Data
• A Stochastic Memoizer for Sequence Data
• A majorization-minimization algorithm for (multiple) hyperparameter learning
• A simpler unified analysis of Budget Perceptrons
• ABC-Boost: Adaptive Base Class Boost for Multi-class Classification
• Accelerated Sampling for the Indian Buffet Process
• Accounting for Burstiness in Topic Models
• Active Learning for Directed Exploration of Complex Systems
• An Accelerated Gradient Method for Trace Norm Minimization
• An Efficient Projection for l1, Regularization
• An Efficient Sparse Metric Learning in High-Dimensional Space via 1 -Penalized Log-Determinant Regularization
• Analytic Moment-based Gaussian Process Filtering
• Archipelago: Nonparametric Bayesian Semi-Supervised Learning
• Bandit-Based Optimization on Graphs with Application to Library Performance Tuning
• Bayesian Clustering for Email Campaign Detection
• Bayesian inference for Plackett-Luce ranking models
• Binary Action Search for Learning Continuous-Action Control Policies
• Block-Wise Construction of Acyclic Relational Features with Monotone Irreducibility and Relevancy Properties
• Blockwise Coordinate Descent Procedures for the Multi-task Lasso, with Applications to Neural Semantic Basis Discovery
• BoltzRank: Learning to Maximize Expected Ranking Gain
• Boosting products of base classifiers
• Compositional Noisy-Logical Learning
• Constraint Relaxation in Approximate Linear Programs
• Convex Variational Bayesian Inference for Large Scale Generalized Linear Models
• Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations
• Curriculum Learning
• Decision Tree and Instance-Based Learning for Label Ranking
• Deep Learning from Temporal Coherence in Video
• Deep Transfer via Second-Order Markov Logic
• Detecting the Direction of Causal Time Series
• Discovering Options from Example Trajectories
• Discriminative k-metrics
• Domain Adaptation from Multiple Sources via Auxiliary Classifiers
• Dynamic Analysis of Multiagent Q-learning with Exploration
• Dynamic Mixed Membership Blockmodel for Evolving Networks
• Efficient Euclidean Projections in Linear Time
• Efficient learning algorithms for changing environments
• EigenTransfer: A Unified Framework for Transfer Learning
• Evaluation Methods for Topic Models
• Exploiting Sparse Markov and Covariance Structure in Multiresolution Models
• Factored Conditional Restricted Boltzmann Machines for Modeling Motion Style
• Fast Evolutionary Maximum Margin Clustering
• Feature Hashing for Large Scale Multitask Learning
• Fitting a Graph to Vector Data
• Function factorization using warped Gaussian processes
• GAODE and HAODE: Two Proposals based on AODE to Deal with Continuous Variables
• Generalization Analysis of Listwise Learning-to-Rank Algorithms
• Geometry-aware Metric Learning
• Good Learners for Evil Teachers
• Gradient Descent with Sparsification: An iterative algorithm for sparse recovery with restricted isometry property
• Grammatical Inference as a Principal Component Analysis Problem
• Graph Construction and b-Matching for Semi-Supervised Learning
• Group Lasso with Overlap and Graph Lasso
• Herding Dynamical Weights to Learn
• Hilbert Space Embeddings of Conditional Distributions with Applications to Dynamical Systems
• Hoeffding and Bernstein Races for Selecting Policies in Evolutionary Direct Policy Search
• Identifying Suspicious URLs: An Application of Large-Scale Online Learning
• Importance Weighted Active Learning
• Incorporating Domain Knowledge into Topic Modeling via Dirichlet Forest Priors
• Independent Factor Topic Models
• Information Theoretic Measures for Clusterings Comparison: Is a Correction for Chance Necessary?
• Interactively Optimizing Information Retrieval Systems as a Dueling Bandits Problem
• K-means in Space: A Radiation Sensitivity Evaluation
• Kernelized Value Function Approximation for Reinforcement Learning
• Large Margin Training for Hidden Markov Models with Partially Observed States
• Large-scale Collaborative Prediction Using a Nonparametric Random Effects Model
• Large-scale Deep Unsupervised Learning using Graphics Processors
• Learning Complex Motions by Sequencing Simpler Motion Templates
• Learning Dictionaries of Stable Autoregressive Models for Audio Scene Analysis
• Learning From Measurements in Exponential Families
• Learning Instance Specific Distances Using Metric Propagation
• Learning Kernels from Indefinite Similarities
• Learning Linear Dynamical Systems without Sequence Information
• Learning Markov Logic Network Structure via Hypergraph Lifting
• Learning Non-Redundant Codebooks for Classifying Complex Objects
• Learning Nonlinear Dynamic Models
• Learning Prediction Suffix Trees with Winnow
• Learning Spectral Graph Transformations for Link Prediction
• Learning Structural SVMs with Latent Variables
• Learning When to Stop Thinking and Do Something!
• Learning structurally consistent undirected probabilistic graphical models
• Learning to Segment from a Few Well-Selected Training Images
• Learning with Structured Sparsity
• Matrix Updates for Perceptron Training of Continuous Density Hidden Markov Models
• MedLDA: Maximum Margin Supervised Topic Models for Regression and Classification
• Model-Free Reinforcement Learning as Mixture Learning
• Monte-Carlo Simulation Balancing
• More Generality in Efficient Multiple Kernel Learning
• Multi-Assignment Clustering for Boolean Data
• Multi-Instance Learning by Treating Instances As Non-I.I.D. Samples
• Multi-View Clustering via Canonical Correlation Analysis
• Multi-class image segmentation using Conditional Random Fields and Global Classification
• Multiple Indefinite Kernel Learning with Mixed Norm Regularization
• Near-Bayesian Exploration in Polynomial Time
• Nearest Neighbors in High-Dimensional Data: The Emergence and Influence of Hubs
• Non-Monotonic Feature Selection
• Non-linear Matrix Factorization with Gaussian Processes
• Nonparametric Estimation of the Precision-Recall Curve
• Nonparametric Factor Analysis with Beta Process Priors
• On Primal and Dual Sparsity of Markov Networks
• On Sampling-based Approximate Spectral Decomposition
• Online Dictionary Learning for Sparse Coding
• Online Feature Elicitation in Interactive Optimization
• Online Learning by Ellipsoid Method
• Optimal Reverse Prediction
• Optimistic Initialization and Greediness Lead to Polynomial Time Learning in Factored MDPs
• Optimized Expected Information Gain for Nonlinear Dynamical Systems
• Orbit-Product Representation and Correction of Gaussian Belief Propagation
• PAC-Bayesian Learning of Linear Classifiers
• Partial Order Embedding with Multiple Kernels
• Partially Supervised Feature Selection with Regularized Linear Models
• Piecewise-stationary Bandit Problems with Side Observations
• Polyhedral Outer Approximations with Application to Natural Language Parsing
• Predictive Representations for Policy Gradient in POMDPs
• Probabilistic Dyadic Data Analysis with Local and Global Consistency
• Proto-Predictive Representation of States with Simple Recurrent Temporal-Difference Networks
• Prototype Vector Machine for Large Scale Semi-Supervised Learning
• Proximal regularization for online and batch learning
• Ranking Interesting Subgroups
• Ranking with Ordered Weighted Pairwise Classification
• Regression by dependence minimization and its application to causal inference in additive noise models
• Regularization and Feature Selection in Least-Squares Temporal Difference Learning
• Robot Trajectory Optimization using Approximate Inference
• Robust Bounds for Classification via Selective Sampling
• Robust Feature Extraction via Information Theoretic Learning
• Route Kernels for Trees
• Rule Learning with Monotonicity Constraints
• Semi-Supervised Learning Using Label Mean
• Sequential Bayesian Prediction in the Presence of Changepoints
• SimpleNPKL : Simple Non-Parametric Kernel Learning
• Solution Stability in Linear Programming Relaxations: Graph Partitioning and Unsupervised Learning
• Sparse Gaussian Graphical Models with Unknown Block Structure
• Sparse Higher Order Conditional Random Fields for improved sequence labeling
• Spectral Clustering based on the graph p-Laplacian
• Split Variational Inference
• Stochastic Methods for
• Stochastic Search using the Natural Gradient
• Structure Learning of Bayesian Networks using Constraints
• Structure Preserving Embedding
• Structure learning with independent non-identically distributed data
• Supervised Learning from Multiple Experts: Whom to trust when everyone lies a bit
• Surrogate Regret Bounds for Proper Losses
• The Adaptive k-Meteorologists Problem and Its Application to Structure Learning and Feature Selection in Reinforcement Learning
• The Bayesian Group-Lasso for Analyzing Contingency Tables
• The Graphlet Spectrum
• Topic-Link LDA: Joint Models of Topic and Author Community
• Tractable Nonparametric Bayesian Inference in Poisson Processes with Gaussian Process Intensities
• Trajectory Prediction: Learning to Map Situations to Robot Trajectories
• Transfer Learning for Collaborative Filtering via a Rating-Matrix Generative Model
• Uncertainty Sampling and Transductive Experimental Design for Active Dual Supervision
• Unsupervised Hierarchical Modeling of Locomotion Styles
• Unsupervised Search-based Structured Prediction
• Using Fast Weights to Improve Persistent Contrastive Divergence