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