ICML Discuss
All Papers at
ICML 2012
Papers starting with:
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T
U
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W
A
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting
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A Hybrid Algorithm for Convex Semidefinite Optimization
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A Complete Analysis of the $l_{1,p}$ Group-Lasso
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A Graphical Model Formulation of Collaborative Filtering Neighbourhood Methods with Fast Maximum Entropy Training
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Active Learning for Matching Problems
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Adaptive Regularization for Similarity Measures
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Approximate Dynamic Programming By Minimizing Distributionally Robust Bounds
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A Generalized Loop Correction Method for Approximate Inference in Graphical Models
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Analysis of Kernel Mean Matching under Covariate Shift
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Approximate Principal Direction Trees
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A Bayesian Approach to Approximate Joint Diagonalization of Square Matrices
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A Proximal-Gradient Homotopy Method for the L1-Regularized Least-Squares Problem
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A Hierarchical Dirichlet Process Model with Multiple Levels of Clustering for Human EEG Seizure Modeling
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A Combinatorial Algebraic Approach for the Identifiability of Low-Rank Matrix Completion
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An Online Boosting Algorithm with Theoretical Justifications
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A Split-Merge Framework for Comparing Clusterings
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Adaptive Canonical Correlation Analysis Based On Matrix Manifolds
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AOSO-LogitBoost: Adaptive One-Vs-One LogitBoost for Multi-Class Problem
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A Topic Model for Melodic Sequences
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An Efficient Approach to Sparse Linear Discriminant Analysis
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Approximate Modified Policy Iteration
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A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound
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A convex relaxation for weakly supervised classifiers
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A Binary Classification Framework for Two-Stage Multiple Kernel Learning
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A Dantzig Selector Approach to Temporal Difference Learning
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Anytime Marginal MAP Inference
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Apprenticeship Learning for Model Parameters of Partially Observable Environments
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Agglomerative Bregman Clustering
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An Infinite Latent Attribute Model for Network Data
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An Iterative Locally Linear Embedding Algorithm
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A Joint Model of Language and Perception for Grounded Attribute Learning
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Agnostic System Identification for Model-Based Reinforcement Learning
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An adaptive algorithm for finite stochastic partial monitoring
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A fast and simple algorithm for training neural probabilistic language models
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A Unified Robust Classification Model
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A Convex Feature Learning Formulation for Latent Task Structure Discovery
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A Discrete Optimization Approach for Supervised Ranking with an Application to Reverse-Engineering Quality Ratings
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A Generative Process for Contractive Auto-Encoders
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B
Bayesian Watermark Attacks
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Bayesian Efficient Multiple Kernel Learning
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Bayesian Conditional Cointegration
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Batch Active Learning via Coordinated Matching
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Bayesian Optimal Active Search and Surveying
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Building high-level features using large scale unsupervised learning
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Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring
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Bayesian Nonexhaustive Learning for Online Discovery and Modeling of Emerging Classes
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Bounded Planning in Passive POMDPs
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C
Capturing topical content with frequency and exclusivity
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Clustering to Maximize the Ratio of Split to Diameter
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Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models
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Compact Hyperplane Hashing with Bilinear Functions
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Conditional Likelihood Maximization: A Unifying Framework for Information Theoretic Feature Selection
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Complexity Analysis of the Lasso Regularization Path
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Clustering using Max-norm Constrained Optimization
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Consistent Covariance Selection From Data With Missing Values
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Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems
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Copula-based Kernel Dependency Measures
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Continuous Inverse Optimal Control with Locally Optimal Examples
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Clustering by Low-Rank Doubly Stochastic Matrix Decomposition
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Convex Multitask Learning with Flexible Task Clusters
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Comparison-Based Learning with Rank Nets
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Communications Inspired Linear Discriminant Analysis
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Copula Mixture Model for Dependency-seeking Clustering
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Compositional Planning Using Optimal Option Models
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Canonical Trends: Detecting Trend Setters in Web Data
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Consistent Multilabel Ranking through Univariate Losses
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Cross-Domain Multitask Learning with Latent Probit Models
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Conditional Sparse Coding and Grouped Multivariate Regression
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Cross Language Text Classification via Subspace Co-regularized Multi-view Learning
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Convergence of the EM Algorithm for Gaussian Mixtures with Unbalanced Mixing Coefficients
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Conditional mean embeddings as regressors
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Conversational Speech Transcription Using Context-Dependent Deep Neural Networks
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D
Distributed Tree Kernels
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Decoupling Exploration and Exploitation in Multi-Armed Bandits
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Deep Mixtures of Factor Analysers
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Dimensionality Reduction by Local Discriminative Gaussians
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Dirichlet Process with Mixed Random Measures: A Nonparametric Topic Model for Labeled Data
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Discriminative Probabilistic Prototype Learning
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Dependent Hierarchical Normalized Random Measures for Dynamic Topic Modeling
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Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events
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Discovering Support and Affiliated Features from Very High Dimensions
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Distributed Parameter Estimation via Pseudo-likelihood
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Deep Lambertian Networks
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Data-driven Web Design
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E
Efficient Decomposed Learning for Structured Prediction
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Ensemble Methods for Convex Regression with Applications to Geometric Programming Based Circuit Design
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Efficient Euclidean Projections onto the Intersection of Norm Balls
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Evaluating Bayesian and L1 Approaches for Sparse Unsupervised Learning
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Efficient Active Algorithms for Hierarchical Clustering
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Efficient Structured Prediction with Latent Variables for General Graphical Models
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Exact Maximum Margin Structure Learning of Bayesian Networks
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Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization
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Estimation of Simultaneously Sparse and Low Rank Matrices
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Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations
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Exact Soft Confidence-Weighted Learning
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Estimating the Hessian by Back-propagating Curvature
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Exemplar-SVMs for Visual Ob ject Detection, Label Transfer and Image Retrieval
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F
Fast Bounded Online Gradient Descent Algorithms for Scalable Kernel-Based Online Learning
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Fast Training of Nonlinear Embedding Algorithms
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Fast Computation of Subpath Kernel for Trees
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Fast Prediction of New Feature Utility
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Factorized Asymptotic Bayesian Hidden Markov Models
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Finding Botnets Using Minimal Graph Clusterings
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Fast classification using sparse decision DAGs
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Flexible Modeling of Latent Task Structures in Multitask Learning
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Fast approximation of matrix coherence and statistical leverage
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Feature Selection via Probabilistic Outputs
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G
Groupwise Constrained Reconstruction for Subspace Clustering
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Gaussian Process Regression Networks
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Group Sparse Additive Models
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Gap Filling in the Plant Kingdom---Trait Prediction Using Hierarchical Probabilistic Matrix Factorization
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Greedy Algorithms for Sparse Reinforcement Learning
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Gaussian Process Quantile Regression using Expectation Propagation
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H
Hypothesis testing using pairwise distances and associated kernels
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How To Grade a Test Without Knowing the Answers --- A Bayesian Graphical Model for Adaptive Crowdsourcing and Aptitude Testing
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Hybrid Batch Bayesian Optimization
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High-Dimensional Covariance Decomposition into Sparse Markov and Independence Domains
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Hierarchical Exploration for Accelerating Contextual Bandits
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I
Improved Information Gain Estimates for Decision Tree Induction
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Improved Nystrom Low-rank Decomposition with Priors
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Influence Maximization in Continuous Time Diffusion Networks
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Infinite-Word Topic Models
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Is margin preserved after random projection?
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Infinite Tucker Decomposition: Nonparametric Bayesian Models for Multiway Data Analysis
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Inferring Latent Structure From Mixed Real and Categorical Relational Data
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Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation
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Incorporating Domain Knowledge in Matching Problems via Harmonic Analysis
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Improved Estimation in Time Varying Models
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Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral Graphs
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Isoelastic Agents and Wealth Updates in Machine Learning Markets
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J
Joint Optimization and Variable Selection of High-dimensional Gaussian Processes
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L
Latent Collaborative Retrieval
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Lightning Does Not Strike Twice: Robust MDPs with Coupled Uncertainty
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Learning to Identify Regular Expressions that Describe Email Campaigns
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Linear Off-Policy Actor-Critic
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Learning the Dependence Graph of Time Series with Latent Factors
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Learning to Label Aerial Images from Noisy Data
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Learning Efficient Structured Sparse Models
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Learning with Augmented Features for Heterogeneous Domain Adaptation
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LPQP for MAP: Putting LP Solvers to Better Use
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Linear Regression with Limited Observation
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Learning the Experts for Online Sequence Prediction
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Learning Force Control Policies for Compliant Robotic Manipulation
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Learning Invariant Representations with Local Transformations
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Lognormal and Gamma Mixed Negative Binomial Regression
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Levy Measure Decompositions for the Beta and Gamma Processes
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Learning Task Grouping and Overlap in Multi-task Learning
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Large-Scale Feature Learning With Spike-and-Slab Sparse Coding
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Learning Object Arrangements in 3D Scenes using Human Context
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Large Scale Variational Bayesian Inference for Structured Scale Mixture Models
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Learning Parameterized Skills
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Latent Multi-group Membership Graph Model
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Local Loss Optimization in Operator Models: A New Insight into Spectral Learning
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Learning the Central Events and Participants in Unlabeled Text
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M
Multiple Kernel Learning from Noisy Labels by Stochastic Programming
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Multi-level Lasso for Sparse Multi-task Regression
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Modeling Latent Variable Uncertainty for Loss-based Learning
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Machine Learning that Matters
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Modelling transition dynamics in MDPs with RKHS embeddings
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Max-Margin Nonparametric Latent Feature Models for Link Prediction
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Marginalized Denoising Autoencoders for Domain Adaptation
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Monte Carlo Bayesian Reinforcement Learning
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Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription
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Modeling Images using Transformed Indian Buffet Processes
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Maximum Margin Output Coding
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Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss
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Manifold Relevance Determination
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N
Nonparametric variational inference
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No-Regret Learning in Extensive-Form Games with Imperfect Recall
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Near-Optimal BRL using Optimistic Local Transitions
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Nonparametric Link Prediction in Dynamic Networks
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O
On multi-view feature learning
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On-Line Portfolio Selection with Moving Average Reversion
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Optimizing F-measure: A Tale of Two Approaches
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On the Size of the Online Kernel Sparsification Dictionary
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On the Partition Function and Random Maximum A-Posteriori Perturbations
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Online Alternating Direction Method
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On the Difficulty of Nearest Neighbor Search
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Output Space Search for Structured Prediction
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On causal and anticausal learning
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On the Sample Complexity of Reinforcement Learning with a Generative Model
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On the Equivalence between Herding and Conditional Gradient Algorithms
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Online Structured Prediction via Coactive Learning
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Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret
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On Local Regret
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P
Path Integral Policy Improvement with Covariance Matrix Adaptation
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Projection-free Online Learning
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PAC Subset Selection in Stochastic Multi-armed Bandits
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Predicting accurate probabilities with a ranking loss
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PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification
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Policy Gradients with Variance Related Risk Criteria
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Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization
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Plug-in martingales for testing exchangeability on-line
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Predicting Consumer Behavior in Commerce Search
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Poisoning Attacks against Support Vector Machines
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Predicting Manhole Events in New York City
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Q
Quasi-Newton Methods: A New Direction
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R
Residual Components Analysis
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Robust PCA in High-dimension: A Deterministic Approach
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Robust Multiple Manifold Structure Learning
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Revisiting k-means: New Algorithms via Bayesian Nonparametrics
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Randomized Smoothing for (Parallel) Stochastic Optimization
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Robust Classification with Adiabatic Quantum Optimization
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Rethinking Collapsed Variational Bayes Inference for LDA
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Regularizers versus Losses for Nonlinear Dimensionality Reduction: A Factored View with New Convex Relaxations
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S
Stability of matrix factorization for collaborative filtering
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Submodular Inference of Diffusion Networks from Multiple Trees
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Stochastic Smoothing for Nonsmooth Minimizations: Accelerating SGD by Exploiting Structure
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Scene parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers
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Sparse-GEV: Sparse Latent Space Model for Multivariate Extreme Value Time Serie Modeling
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Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching
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State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction
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Sparse Additive Functional and Kernel CCA
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Semi-Supervised Collective Classification via Hybrid Label Regularization
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Subgraph Matching Kernels for Attributed Graphs
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Shortest path distance in random k-nearest neighbor graphs
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Sparse Support Vector Infinite Push
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Small-sample brain mapping: sparse recovery on spatially correlated designs with randomization and clustering
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Scaling Up Coordinate Descent Algorithms for Large $\ell_1$ Regularization Problems
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Semi-supervised Metric Learning Paradigm with Hyper-Sparsity
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Structured Learning from Partial Annotations
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Statistical linear estimation with penalized estimators: an application to reinforcement learning
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Sequential Nonparametric Regression
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Sparse stochastic inference for latent Dirichlet allocation
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Smoothness and Structure Learning by Proxy
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Safe Exploration in Markov Decision Processes
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Similarity Learning for Provably Accurate Sparse Linear Classification
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T
TrueLabel + Confusions: A Spectrum of Probabilistic Models in Analyzing Multiple Ratings
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Total Variation and Euler's Elastica for Supervised Learning
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To Average or Not to Average? Making Stochastic Gradient Descent Optimal for Strongly Convex Problems
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The Most Persistent Soft-Clique in a Set of Sampled Graphs
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Two Manifold Problems with Applications to Nonlinear System Identification
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Tighter Variational Representations of f-Divergences via Restriction to Probability Measures
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Training Restricted Boltzmann Machines on Word Observations
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The Convexity and Design of Composite Multiclass Losses
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The Kernelized Stochastic Batch Perceptron
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The Landmark Selection Method for Multiple Output Prediction
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The Greedy Miser: Learning under Test-time Budgets
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The Nonparanormal SKEPTIC
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The Nonparametric Metadata Dependent Relational Model
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The Big Data Bootstrap
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U
Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation
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Using CCA to improve CCA: A new spectral method for estimating vector models of words
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Utilizing Static Analysis and Code Generation to Accelerate Neural Networks
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V
Variational Bayesian Inference with Stochastic Search
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Variance Function Estimation in High-dimensions
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Variational Inference in Non-negative Factorial Hidden Markov Models for Efficient Audio Source Separation
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W
Which Statistical Estimators Have Differentially Private Approximations?
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Created by
Mark Reid
for
ICML