WhatToSee
Index of
ICML 2008
-Support Vector Machine as Conditional Value-at-Risk Minimization
A Decoupled Approach to Exemplar-based Unsup ervised Learning
A Distance Mo del for Rhythms
A Dual Co ordinate Descent Metho d for Large-scale Linear SVM
A Generalization of Haussler's Convolution Kernel -- Mapping Kernel
A Least Squares Formulation for Canonical Correlation Analysis
A Quasi-Newton Approach to Nonsmo oth Convex Optimization
A Rate-Distortion One-Class Mo del and its Applications to Clustering
A Repro ducing Kernel Hilb ert Space Framework for Pairwise Time Series Distances
A Semiparametric Statistical Approach to Mo del-Free Policy Evaluation
A Unified Architecture for Natural Language Pro cessing: Deep Neural Networks with Multitask Learning
A Worst-Case Comparison b etween Temp oral Difference and Residual Gradient with Linear Function Approximation
Accurate Max-Margin Training for Structured Output Spaces
Active Kernel Learning
Active Reinforcement Learning
Actively Learning Level-Sets of Composite Functions
Adaptive p-Posterior Mixture-Mo del Kernels for Multiple Instance Learning
An Analysis of Linear Models, Linear Value-Function Approximation, and Feature Selection for Reinforcement Learning
An Analysis of Reinforcement Learning with Function Approximation
An Asymptotic Analysis of Generative, Discriminative, and Pseudolikeliho o d Estimators
An Empirical Evaluation of Sup ervised Learning in High Dimensions
An HDP-HMM for Systems with State Persistence
An Object-Oriented Representation for Efficient Reinforcement Learning
An RKHS for Multi-View Learning and Manifold Co-Regularization
Apprenticeship Learning Using Linear Programming
Automatic Discovery and Transfer of MAXQ Hierarchies
Autonomous Geometric Precision Error Estimation in Low-Level Computer Vision Tasks
Bayes Optimal Classification for Decision Trees
Bayesian Multiple Instance Learning: Automatic Feature Selection and Inductive Transfer
Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo
Beam Sampling for the Infinite Hidden Markov Mo del
Bi-Level Path Following for Cross Validated Solution of Kernel Quantile Regression
Bolasso: Model Consistent Lasso Estimation through the Bootstrap
Boosting with Incomplete Information
Causal Mo delling Combining Instantaneous and Lagged Effects: an Identifiable Mo del Based on Non-Gaussianity
Classification using Discriminative Restricted Boltzmann Machines
Closed-Form Sup ervised Dimensionality Reduction with Generalized Linear Mo dels
Composite Kernel Learning
Compressed Sensing and Bayesian Exp erimental Design
Cost-sensitive Multi-class Classification from Probability Estimates
Data Sp ectroscopy: Learning Mixture Mo dels using Eigenspaces of Convolution Op erators
Deep Learning via Semi-Sup ervised Emb edding
Democratic Approximation of Lexicographic Preference Models
Detecting Statistical Interactions with Additive Groves of Trees
Dirichlet Comp onent Analysis: Feature Extraction for Comp ositional Data
Discriminative Parameter Learning for Bayesian Networks
Discriminative Structure and Parameter Learning for Markov Logic Networks
Efficient Bandit Algorithms for Online Multiclass Prediction
Efficient MultiClass Maximum Margin Clustering
Efficient Projections onto the 1 -Ball for Learning in High Dimensions
Efficiently Learning Linear-Linear Exp onential Family Predictive Representations of State
Efficiently Solving Convex Relaxations for MAP Estimation
Empirical Bernstein Stopping
Estimating Lab els from Lab el Prop ortions
Estimating Lo cal Optimums in EM Algorithm over Gaussian Mixture Mo del
Expectation-Maximization for Sparse and Non-Negative PCA
Exploration Scavenging
Extracting and Comp osing Robust Features with Denoising Auto enco ders
Fast Estimation of First-Order Clause Coverage through Randomization and Maximum Likeliho o d
Fast Gaussian Process Methods for Point Process Intensity Estimation
Fast Incremental Proximity Search in Large Graphs
Fast Nearest Neighb or Retrieval for Bregman Divergences
Fast Solvers and Efficient Implementations for Distance Metric Learning
Fast Supp ort Vector Machine Training and Classification on Graphics Pro cessors
Fully Distributed EM for Very Large Datasets
Gaussian Pro cess Pro duct Mo dels for Nonparametric Nonstationarity
Graph Kernels between Point Clouds
Graph Transduction via Alternating Minimization
Grassmann Discriminant Analysis: a Unifying View on Subspace-Based Learning
Hierarchical Kernel Stick-Breaking Process for Multi-Task Image Analysis
Hierarchical Model-Based Reinforcement Learning: R - M A X + MAXQ
Hierarchical Sampling for Active Learning
ICA and ISA Using Schweizer-Wolff Measure of Dep endence
Inverting the Viterbi Algorithm: An Abstract Framework for Structure Design
Knows What It Knows: A Framework For Self-Aware Learning
Laplace Maximum Margin Markov Networks
Large Scale Manifold Transduction
Learning All Optimal Policies with Multiple Criteria
Learning Dissimilarities by Ranking: From SDP to QP
Learning Diverse Rankings with Multi-Armed Bandits
Learning for Control from Multiple Demonstrations
Learning from Incomplete Data with Infinite Imputations
Learning to Classify with Missing and Corrupted Features
Learning to Learn Implicit Queries from Gaze Patterns
Learning to Sportscast: A Test of Grounded Language Acquisition
Listwise Approach to Learning to Rank - Theory and Algorithm
Local Likeliho o d Mo deling of Temp oral Text Streams
Localized Multiple Kernel Learning
Manifold Alignment using Pro crustes Analysis
ManifoldBoost: Stagewise Function Approximation for Fully-, Semiand Un-sup ervised Learning
Maximum Likeliho o d Rule Ensembles
Memory Bounded Inference in Topic Models
Message-passing for Graph-structured Linear Programs: Proximal Pro jections, Convergence and Rounding Schemes
Metric Embedding for Kernel Classification Rules
Modeling Interleaved Hidden Pro cesses
Modified MMI/MPE: A Direct Evaluation of the Margin in Speech Recognition
Multi-Classification by Categorical Features via Clustering
Multi-Task Compressive Sensing with Dirichlet Process Priors
Multi-Task Learning for HIV Therapy Screening
Multiple Instance Ranking
Nearest Hyperdisk Methods for High-Dimensional Classification
No-Regret Learning in Convex Games
Non-Parametric Policy Gradients: A Unified Treatment of Prop ositional and Relational Domains
Nonextensive Entropic Kernels
Nonnegative Matrix Factorization via Rank-One Downdate
On Multi-View Active Learning and the Combination with Semi-Sup ervised Learning
On Partial Optimality in Multi-lab el MRFs
On the Chance Accuracies of Large Collections of Classifiers
On the Hardness of Finding Symmetries in Markov Decision Processes
On the Quantitative Analysis of Deep Belief Networks
On-line Discovery of Temp oral-Difference Networks
Online Kernel Selection for Bayesian Reinforcement Learning
Optimized Cutting Plane Algorithm for Support Vector Machines
Optimizing Estimated Loss Reduction for Active Sampling in Rank Learning
Pairwise Constraint Propagation by Semidefinite Programming for Semi-Sup ervised Classification
Pointwise Exact Bo otstrap Distributions of Cost Curves
Polyhedral Classifier for Target Detection A Case Study: Colorectal Cancer
Preconditioned Temp oral Difference Learning
Predicting Diverse Subsets Using Structural SVMs
Prediction with Exp ert Advice for the Brier Game
Privacy-Preserving Reinforcement Learning
Query-Level Stability and Generalization in Learning to Rank
Random Classification Noise Defeats All Convex Potential Bo osters
Rank Minimization via Online Learning
Reinforcement Learning in the Presence of Rare Events
Reinforcement Learning with Limited Reinforcement: Using Bayes Risk for Active Learning in POMDPs
Robust Matching and Recognition using Context-Dep endent Kernels
SVM Optimization: Inverse Dependence on Training Set Size
Sample-Based Learning and Search with Permanent and Transient Memories
Self-taught Clustering
Semi-supervised Learning of Compact Do cument Representations with Deep Networks
Sequence Kernels for Predicting Protein Essentiality
Sparse Bayesian Nonparametric Regression
Sparse Multiscale Gaussian Pro cess Regression
Spectral Clustering with Inconsistent Advice
Stability of Transductive Regression Algorithms
Statistical Mo dels for Partial Memb ership
Stopping Conditions for Exact Computation of Leave-One-Out Error in Support Vector Machines
Strategy Evaluation in Extensive Games with Importance Sampling
Structure Compilation: Trading Structure for Features
Tailoring Density Estimation via Reproducing Kernel Moment Matching
The Asymptotics of Semi-Sup ervised Learning in Discriminative Probabilistic Mo dels
The Dynamic Hierarchical Dirichlet Pro cess
The Group-Lasso for Generalized Linear Models: Uniqueness of Solutions and Efficient Algorithms
The Many Faces of Optimism: a Unifying Approach
The Pro jectron: a Bounded Kernel-Based Perceptron
The Skew Sp ectrum of Graphs
Training Restricted Boltzmann Machines using Approximations to the Likeliho o d Gradient
Training SVM with Indefinite Kernels
Training Structural SVMs when Exact Inference is Intractable
Transfer of Samples in Batch Reinforcement Learning
Uncorrelated Multilinear Principal Comp onent Analysis through Successive Variance Maximization
Unsupervised Rank Aggregation with Distance-Based Mo dels
mStruct: A New Admixture Mo del for Inference of Population Structure in Light of Both Genetic Admixing and Allele Mutations
¨ Improved Nystrom Low-Rank Approximation and Error Analysis