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