oO(ML Discuss)
All Papers at
ICML 2011
Papers starting with:
A
B
C
D
E
F
G
H
I
K
L
M
N
O
P
R
S
T
U
V
A
A Co-training Approach for Multi-view Spectral Clustering
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A Coherent Interpretation of AUC as a Measure of Aggregated Classification Performance
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A Graph-based Framework for Multi-Task Multi-View Learning
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A New Bayesian Rating System for Team Competitions
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A PAC-Bayes Sample-compression Approach to Kernel Methods
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A Spectral Algorithm for Latent Tree Graphical Models
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A Three-Way Model for Collective Learning on Multi-Relational Data
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A Unified Probabilistic Model for Global and Local Unsupervised Feature Selection
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ABC-EP: Expectation Propagation for Likelihood-free Bayesian Computation
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Access to Unlabeled Data can Speed up Prediction Time
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Active Learning from Crowds
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Adaptive Kernel Approximation for Large-Scale Non-Linear SVM Prediction
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Adaptively Learning the Crowd Kernel
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An Augmented Lagrangian Approach to Constrained MAP Inference
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Apprenticeship Learning About Multiple Intentions
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Approximate Dynamic Programming for Storage Problems
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Approximating Correlated Equilibria using Relaxations on the Marginal Polytope
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Approximation Bounds for Inference using Cooperative Cuts
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Automatic Feature Decomposition for Single View Co-training
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B
BCDNPKL: Scalable Non-Parametric Kernel Learning Using Block Coordinate Descent
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Bayesian CCA via Group Sparsity
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Bayesian Learning via Stochastic Gradient Langevin Dynamics
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Beam Search based MAP Estimates for the Indian Buffet Process
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Beat the Mean Bandit
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Better Algorithms for Selective Sampling
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Bipartite Ranking through Minimization of Univariate Loss
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Boosting on a Budget: Sampling for Feature-Efficient Prediction
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Bounding the Partition Function using Holder's Inequality
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Brier Curves: a New Cost-Based Visualisation of Classifier Performance
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Bundle Selling by Online Estimation of Valuation Functions
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C
Cauchy Graph Embedding
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Classification-based Policy Iteration with a Critic
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Clustering Partially Observed Graphs via Convex Optimization
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Clustering by Left-Stochastic Matrix Factorization
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Clusterpath: an Algorithm for Clustering using Convex Fusion Penalties
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Computational Rationalization: The Inverse Equilibrium Problem
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Conjugate Markov Decision Processes
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Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
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Convex Max-Product over Compact Sets for Protein Folding
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D
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach
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Doubly Robust Policy Evaluation and Learning
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Dynamic Egocentric Models for Citation Networks
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Dynamic Tree Block Coordinate Ascent
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E
Efficient Rule Ensemble Learning using Hierarchical Kernels
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Efficient Sparse Modeling with Automatic Feature Grouping
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Eigenvalue Sensitive Feature Selection
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Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines
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Estimating the Bayes Point Using Linear Knapsack Problems
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F
Fast Global Alignment Kernels
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Fast Newton-type Methods for Total Variation Regularization
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Finite-Sample Analysis of Lasso-TD
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From PAC-Bayes Bounds to Quadratic Programs for Majority Votes
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Functional Regularized Least Squares Classication with Operator-valued Kernels
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G
Generalized Boosting Algorithms for Convex Optimization
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Generalized Value Functions for Large Action Sets
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Generating Text with Recurrent Neural Networks
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GoDec: Randomized Low-rank & Sparse Matrix Decomposition in Noisy Case
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H
Hashing with Graphs
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Hierarchical Classification via Orthogonal Transfer
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I
Implementing regularization implicitly via approximate eigenvector computation
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Incremental Basis Construction from Temporal Difference Error
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Inference of Inversion Transduction Grammars
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Infinite Dynamic Bayesian Networks
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Infinite SVM: a Dirichlet Process Mixture of Large-margin Kernel Machines
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Integrating Partial Model Knowledge in Model Free RL Algorithms
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K
k-DPPs: Fixed-Size Determinantal Point Processes
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L
Large Scale Text Classification using Semi-supervised Multinomial Naive Bayes
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Large-Scale Convex Minimization with a Low-Rank Constraint
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Large-Scale Learning of Embeddings with Reconstruction Sampling
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Learning Deep Energy Models
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Learning Discriminative Fisher Kernels
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Learning Linear Functions with Quadratic and Linear Multiplicative Updates
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Learning Mallows Models with Pairwise Preferences
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Learning Multi-View Neighborhood Preserving Projections
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Learning Output Kernels with Block Coordinate Descent
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Learning Recurrent Neural Networks with Hessian-Free Optimization
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Learning Scoring Functions with Order-Preserving Losses and Standardized Supervision
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Learning attentional policies for tracking and recognition in video with deep networks
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Learning from Multiple Outlooks
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Learning with Whom to Share in Multi-task Feature Learning
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Linear Regression under Fixed-Rank Constraints: A Riemannian Approach
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Locally Linear Support Vector Machines
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M
Manifold Identification of Dual Averaging Methods for Regularized Stochastic Online Learning
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Mapping kernels for trees
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Max-margin Learning for Lower Linear Envelope Potentials in Binary Markov Random Fields
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Mean-Variance Optimization in Markov Decision Processes
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Message Passing Algorithms for the Dirichlet Diffusion Tree
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Minimal Loss Hashing for Compact Binary Codes
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Minimax Learning Rates for Bipartite Ranking and Plug-in Rules
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Minimum Probability Flow Learning
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Multi-Label Classification on Tree- and DAG-Structured Hierarchies
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Multiclass Boosting with Hinge Loss based on Output Coding
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Multiclass Classification with Bandit Feedback using Adaptive Regularization
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Multimodal Deep Learning
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Multiple Instance Learning with Manifold Bags
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N
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions
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O
On Autoencoders and Score Matching for Energy Based Models
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On Bayesian PCA: Automatic Dimensionality Selection and Analytic Solution
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On Information-Maximization Clustering: Tuning Parameter Selection and Analytic Solution
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On Random Weights and Unsupervised Feature Learning
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On optimization methods for deep learning
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On the Integration of Topic Modeling and Dictionary Learning
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On the Necessity of Irrelevant Variables
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On the Robustness of Kernel Density M-Estimators
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On the Use of Variational Inference for Learning Discrete Graphical Models
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On tracking portfolios with certainty equivalents on a generalization of Markowitz model: the Fool, the Wise and the Adaptive
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Online AUC Maximization
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Online Discovery of Feature Dependencies
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Online Submodular Minimization for Combinatorial Structures
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OptiML: An Implicitly Parallel Domain-Specific Language for Machine Learning
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Optimal Distributed Online Prediction
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P
PILCO: A Model-Based and Data-Efficient Approach to Policy Search
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Parallel Coordinate Descent for L1-Regularized Loss Minimization
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Parsing Natural Scenes and Natural Language with Recursive Neural Networks
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Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models
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Predicting Legislative Roll Calls from Text
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Preserving Personalized Pagerank in Subgraphs
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Probabilistic Matrix Addition
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Pruning nearest neighbor cluster trees
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R
Relational Active Learning for Joint Collective Classification Models
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Risk-Based Generalizations of f-divergences
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Robust Matrix Completion and Corrupted Columns
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S
SampleRank: Training Factor Graphs with Atomic Gradients
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Semi-supervised Penalized Output Kernel Regression for Link Prediction
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Simultaneous Learning and Covering with Adversarial Noise
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Size-constrained Submodular Minimization through Minimum Norm Base
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Sparse Additive Generative Models of Text
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Speeding-Up Hoeffding-Based Regression Trees With Options
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Stochastic Low-Rank Kernel Learning for Regression
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Structure Learning in Ergodic Factored MDPs without Knowledge of the Transition Function's In-Degree
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Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection
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Suboptimal Solution Path Algorithm for Support Vector Machine
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Support Vector Machines as Probabilistic Models
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Surrogate losses and regret bounds for cost-sensitive classification with example-dependent costs
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T
Task Space Retrieval Using Inverse Feedback Control
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The Constrained Weight Space SVM: Learning with Ranked Features
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The Hierarchical Beta Process for Convolutional Factor Analysis and Deep Learning
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The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization
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The Infinite Regionalized Policy Representation
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Time Series Clustering: Complex is Simpler!
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Topic Modeling with Nonparametric Markov Tree
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Towards Making Unlabeled Data Never Hurt
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Tree preserving embedding
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Tree-Structured Infinite Sparse Factor Model
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U
Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning
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Uncovering the Temporal Dynamics of Diffusion Networks
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Unimodal Bandits
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Unsupervised Models of Images by Spike-and-Slab RBMs
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V
Variational Heteroscedastic Gaussian Process Regression
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Variational Inference for Policy Search in changing situations
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Variational Inference for Stick-Breaking Beta Process Priors
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Vector-valued Manifold Regularization
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