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ICML 2009 - Accepted Papers
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A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
Index
A
A Bayesian Approach to Protein Model Quality Assessment (2009)
ABC-Boost: Adaptive Base Class Boost for Multi-class Classification (2009)
Accelerated Gibbs Sampling for the Indian Buffet Process (2009)
Accounting for Burstiness in Topic Models (2009)
A Convex Formulation for Learning Shared Structures from Multiple Tasks (2009)
Active Learning for Directed Exploration of Complex Systems (2009)
A Least Squares Formulation for a Class of Generalized Eigenvalue Problems in Machine Learning (2009)
A majorization-minimization algorithm for (multiple) hyperparameter learning (2009)
An Accelerated Gradient Method for Trace Norm Minimization (2009)
Analytic Moment-based Gaussian Process Filtering (2009)
An Efficient Projection for L1,Infinity Regularization (2009)
An Efficient Sparse Metric Learning in High-Dimensional Space via $\ell_1$-Penalized Log-Determinant Regularization (2009)
An Empirical Comparison of Abstraction in Models of Markov Decision Processes (2009)
A Novel Lexicalized HMM-based Learning Framework for Web Opinion Mining (2009)
Approximate Inference for Planning in Stochastic Relational Worlds (2009)
Archipelago: Nonparametric Bayesian Semi-Supervised Learning (2009)
A Scalable Framework for Discovering Coherent Co-clusters in Noisy Data (2009)
A simpler unified analysis of Budget Perceptrons (2009)
A Stochastic Memoizer for Sequence Data (2009)
B
Bandit-Based Optimization on Graphs with Application to Library Performance Tuning (2009)
Bayesian Clustering for Email Campaign Detection (2009)
Bayesian inference for Plackett-Luce ranking models (2009)
Binary Action Search for Learning Continuous-Action Control Policies (2009)
Block-Wise Construction of Acyclic Relational Features with Monotone Irreducibility and Relevancy Properties (2009)
Blockwise Coordinate Descent Procedures for the Multi-task Lasso, with Applications to Neural Semantic Basis Discovery (2009)
BoltzRank: Learning to Maximize Expected Ranking Gain (2009)
Boosting products of base classifiers (2009)
Boosting with Structural Sparsity (2009)
C
Compositional Noisy-Logical Learning (2009)
Constraint Relaxation in Approximate Linear Programs (2009)
Convex Variational Bayesian Inference for Large Scale Generalized Linear Models (2009)
Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations (2009)
Curriculum Learning (2009)
D
Decision Tree and Instance-Based Learning for Label Ranking (2009)
Deep Learning from Temporal Coherence in Video (2009)
Deep Transfer via Second-Order Markov Logic (2009)
Detecting the Direction of Causal Time Series (2009)
Discovering Options from Example Trajectories (2009)
Discriminative $k$ metrics (2009)
Domain Adaptation from Multiple Sources via Auxiliary Classifiers (2009)
Dynamic Analysis of Multiagent Q-learning with e-greedy Exploration (2009)
Dynamic Mixed Membership Blockmodel for Evolving Networks (2009)
E
Efficient Euclidean Projections in Linear Time (2009)
Efficient learning algorithms for changing environments (2009)
EigenTransfer: A Unified Framework for Transfer Learning (2009)
Evaluation Methods for Topic Models (2009)
Exploiting Sparse Markov and Covariance Structure in Multiresolution Models (2009)
F
Factored Conditional Restricted Boltzmann Machines for Modeling Motion Style (2009)
Fast Evolutionary Maximum Margin Clustering (2009)
Fast Gradient-Descent Methods for Temporal-Difference Learning with Linear Function Approximation (2009)
Feature Hashing for Large Scale Multitask Learning (2009)
Finding Equivalences Among Abstract Actions (2009)
Fitting a Graph to Vector Data (2009)
Function factorization using warped Gaussian processes (2009)
G
GAODE and HAODE: Two Proposals based on AODE to Deal with Continuous Variables (2009)
Generalization Analysis of Listwise Learning-to-Rank Algorithms (2009)
Geometry-aware Metric Learning (2009)
Good Learners for Evil Teachers (2009)
Gradient Descent with Sparsification: an iterative algorithm for sparse recovery with restricted isometry property (2009)
Grammatical Inference as a Principal Component Analysis Problem (2009)
Graph Construction and b-Matching for Semi-Supervised Learning (2009)
Group Lasso with Overlaps and Graph Lasso (2009)
H
Herding Dynamic Weights to Learn (2009)
Hierarchical Skill Learning for High-Level Planning (2009)
Hilbert Space Embeddings of Conditional Distributions with Applications to Dynamical Systems (2009)
Hoeffding and Bernstein Races for Selecting Policies in Evolutionary Direct Policy Search (2009)
I
Identifying Suspicious URLs: An Application of Large-Scale Online Learning (2009)
Importance Weighted Active Learning (2009)
Incorporating Domain Knowledge into Topic Modeling via Dirichlet Forest Priors (2009)
Independent Factor Topic Models (2009)
Information Theoretic Measures for Clusterings Comparison: Is a Correction for Chance Necessary? (2009)
Integrating Value Function-Based and Policy Search Methods for Sequential Decision Making (2009)
Interactively Optimizing Information Retrieval Systems as a Dueling Bandits Problem (2009)
K
K-means in Space: A Radiation Sensitivity Evaluation (2009)
Kernelized Value Function Approximation for Reinforcement Learning (2009)
L
Large-scale Collaborative Prediction Using a Nonparametric Random Effects Model (2009)
Large-scale Deep Unsupervised Learning using Graphics Processors (2009)
Large Margin Training for Hidden Markov Models with Partially Observed States (2009)
Learning Complex Motions by Sequencing Simpler Motion Templates (2009)
Learning Dictionaries of Stable Autoregressive Models for Audio Scene Analysis (2009)
Learning from Measurements in Exponential Families (2009)
Learning Instance Specific Distances Using Metric Propagation (2009)
Learning Kernels from Indefinite Similarities (2009)
Learning Linear Dynamical Systems without Sequence Information (2009)
Learning Markov Logic Network Structure via Hypergraph Lifting (2009)
Learning Non-Explicit Control Parameters of Self-Organizing Systems (2009)
Learning Non-Redundant Codebooks for Classifying Complex Objects (2009)
Learning Nonlinear Dynamic Models (2009)
Learning Prediction Suffix Trees with Winnow (2009)
Learning Spectral Graph Transformations for Link Prediction (2009)
Learning structurally consistent undirected probabilistic graphical models (2009)
Learning Structural SVMs with Latent Variables (2009)
Learning to Segment from a Few Well-Selected Training Images (2009)
Learning When to Stop Thinking and Do Something! (2009)
Learning with Structured Sparsity (2009)
Linear Value Function Approximation and Linear Models (2009)
M
Matrix Updates for Perceptron Training of Continuous Density Hidden Markov Models (2009)
MedLDA: Maximum Margin Supervised Topic Models for Regression and Classification (2009)
Model-Free Reinforcement Learning as Mixture Learning (2009)
Monte-Carlo Simulation Balancing (2009)
More Generality in Efficient Multiple Kernel Learning (2009)
Multi-Assignment Clustering for Boolean Data (2009)
Multi-class image segmentation using Conditional Random Fields and Global Classification (2009)
Multi-Instance Learning by Treating Instances As Non-I.I.D. Samples (2009)
Multi-View Clustering via Canonical Correlation Analysis (2009)
Multiple Indefinite Kernel Learning with Mixed Norm Regularization (2009)
Multivariate Time Series Analysis of Physiological and Clinical Data (2009)
N
Near-Bayesian Exploration in Polynomial Time (2009)
Nearest Neighbors in High-Dimensional Data: The Emergence and Influence of Hubs (2009)
Non-linear Matrix Factorization with Gaussian Processes (2009)
Non-Monotonic Feature Selection (2009)
Nonparametric Estimation of the Precision-Recall Curve (2009)
Nonparametric Factor Analysis with Beta Process Priors (2009)
O
Online Dictionary Learning for Sparse Coding (2009)
Online Feature Elicitation in Interactive Optimization (2009)
Online Learning by Ellipsoid Method (2009)
On Primal and Dual Sparsity of Markov Networks (2009)
On Sampling-based Approximate Spectral Decomposition (2009)
Optimal Reverse Prediction: A Unified Perspective on Supervised, Unsupervised and Semi-supervised Learning (2009)
Optimistic Initialization and Greediness Lead to Polynomial Time Learning in Factored MDPs (2009)
Optimized Expected Information Gain for Nonlinear Dynamical Systems (2009)
Orbit-Product Representation and Correction of Gaussian Belief Propagation (2009)
P
PAC-Bayesian Learning of Linear Classifiers (2009)
Partially Supervised Feature Selection with Regularized Linear Models (2009)
Partial Order Embedding with Multiple Kernels (2009)
Piecewise-stationary bandit problems with side observations (2009)
Polyhedral Outer Approximations with Application to Natural Language Parsing (2009)
Predictive Representations for Policy Gradient in POMDPs (2009)
Probabilistic Dyadic Data Analysis with Local and Global Consistency (2009)
Proto-Predictive Representation of States with Simple Recurrent Temporal-Difference Networks (2009)
Prototype Vector Machine for Large Scale Semi-supervised Learning (2009)
Proximal regularization for online and batch learning (2009)
R
Ranking Interesting Subgroups (2009)
Ranking with Ordered Weighted Pairwise Classification (2009)
Regression by dependence minimization and its application to causal inference (2009)
Regularization and Feature Selection in Least-Squares Temporal Difference Learning (2009)
Robot Trajectory Optimization using Approximate Inference (2009)
Robust Bounds for Classification via Selective Sampling (2009)
Robust Feature Extraction via Information Theoretic Learning (2009)
Route Kernels for Trees (2009)
Rule Learning with Monotonicity Constraints (2009)
S
Semi-Supervised Learning Using Label Mean (2009)
Sequential Bayesian Prediction in the Presence of Changepoints (2009)
SimpleNPKL:Simple Non-Parametric Kernel Learning (2009)
Skill Acquisition in Continuous Reinforcement Learning Domains (2009)
Solution Stability in Linear Programming Relaxations: Graph Partitioning and Unsupervised Learning (2009)
Sparse Gaussian Graphical Models with Unknown Block Structure (2009)
Sparse Higher Order Conditional Random Fields for improved sequence labeling (2009)
Spectral Clustering based on the graph p-Laplacian (2009)
Split Variational Inference (2009)
Stochastic Methods for L1 Regularized Loss Minimization (2009)
Stochastic Search using the Natural Gradient (2009)
Structure Learning of Bayesian Networks using Constraints (2009)
Structure learning with independent non-identically distributed data (2009)
Structure Preserving Embedding (2009)
Supervised Learning from Multiple Experts: Whom to trust when everyone lies a bit (2009)
Surrogate Regret Bounds for Proper Losses (2009)
T
The Adaptive k-Meteorologists Problem and Its Application to Structure Learning and Feature Selection in Reinforcement Learning (2009)
The Bayesian Group-Lasso for Analyzing Contingency Tables (2009)
The graphlet spectrum (2009)
Topic-Link LDA: Joint Models of Topic and Author Community (2009)
Tractable Nonparametric Bayesian Inference in Poisson Processes with Gaussian Process Intensities (2009)
Trajectory Prediction: Learning to Map Situations to Robot Trajectories (2009)
Transfer Learning for Collaborative Filtering via a Rating-Matrix Generative Model (2009)
U
Uncertainty Sampling and Transductive Experimental Design for Active Dual Supervision (2009)
Unsupervised Formation of Invariant Concepts from Unstructured Data (2009)
Unsupervised Hierarchical Modeling of Locomotion Styles (2009)
Unsupervised Search-based Structured Prediction (2009)
Using Fast Weights to Improve Persistent Contrastive Divergence (2009)
paper/2009.txt · Last modified: 2009/05/24 17:59 (external edit)
