Accepted Papers
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A
B
C
D
E
F
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H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
Index
A
Accurate max-margin training for structured output spaces
Active Kernel Learning
Actively Learning Level-Sets of Composite Functions
Active Reinforcement Learning
Adaptive p-Posterior Mixture-Model Kernels for Multiple Instance Learning
A Decoupled Approach to Exemplar-based Unsupervised Learning.
A Distance Model for Rhythms
A Dual Coordinate Descent Method for Large-scale Linear SVM
A generalization of Haussler's convolution kernel - mapping kernel
A Least Squares Formulation for Canonical Correlation Analysis
An Analysis of Generative, Discriminative, and Pseudolikelihood Estimators
An Analysis of Linear Models, Linear Value-Function Approximation, and Feature Selection for Reinforcement Learning
An analysis of reinforcement learning with function approximation
An Empirical Evaluation of Supervised 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
A Quasi-Newton Approach to Nonsmooth Convex Optimization
A Rate-Distortion One-Class Model and its Applications to Clustering
A Reproducing Kernel Hilbert Space Framework for Pairwise Time Series Distances
A Semiparametric Statistical Approach to Model-Free Policy Evaluation
A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning
Automatic Discovery and Transfer of MAXQ Hierarchies
Autonomous geometric precision error estimation in low-level computer vision tasks
A Worst-Case Comparison Between Temporal Difference and Residual Gradient with Linear Function Approximation
B
Bayesian multiple instance learning: automatic feature selection and inductive transfer
Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo
Bayes Optimal Classification for Decision Trees
Beam Sampling for the Infinite Hidden Markov Model
Bi-Level Path Following for Cross Validated Solution of Kernel Quantile Regression
Bolasso: Model Consistent Lasso Estimation through the Bootstrap
Boosting with Incomplete Information
C
Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity
Classification using Discriminative Restricted Boltzmann Machines
Closed-form Supervised Dimensionality Reduction with Generalized Linear Models
Composite Kernel Learning
Compressed Sensing and Bayesian Experimental Design
Confidence-Weighted Linear Classification
Cost-Sensitive Multi-class Classification From Probability Estimates
D
Data Spectroscopy: Learning Mixture Models using Eigenspaces of Convolution Operators
Deep Learning via Semi-Supervised Embedding
Democratic Approximation of Lexicographic Preference Models
Detecting Statistical Interactions with Additive Groves of Trees
Dirichlet Component Analysis: Feature Extraction for Compositional Data
Discriminative Parameter Learning for Bayesian Networks
Discriminative Structure and Parameter Learning for Markov Logic Networks
E
Efficient Bandit Algorithms for Online Multiclass Prediction
Efficient Deep Learning for Text Classification and Retrieval
Efficiently Learning Linear-Linear Exponential Family Predictive Representations of State
Efficiently Solving Convex Relaxations for MAP Estimation
Efficient MultiClass Maximum Margin Clustering
Efficient Projections onto the L1-Ball for Learning in High Dimensions
Empirical Bernstein Stopping
Estimating Labels from Label Proportions
Estimating Local Optimums in EM Algorithm over Gaussian Mixture Model
Expectation-Maximization for Sparse and Non-Negative PCA
Exploration Scavenging
Extracting and Composing Robust Features with Denoising Autoencoders
F
Fast Estimation of First-Order Clause Coverage through Randomization and Maximum Likelihood
Fast Gaussian Process Methods for Point Process Intensity Estimation
Fast Incremental Proximity Search in Large Graphs
Fast nearest neighbor retrieval for bregman divergences
Fast Solvers and Efficient Implementations for Distance Metric Learning
Fast Support Vector Machine Training and Classification on Graphics Processors
Fully Distributed EM for Very Large Datasets
G
Gaussian Process Product Models for Nonparametric Nonstationarity
Graph kernels between point clouds
Graph Transduction via Alternating Minimization
Grassmann Discriminant Analysis: a Unifying View on Subspace-Based Learning
H
Hierarchical Kernel Stick-Breaking Process for Multi-Task Image Analysis
Hierarchical Model-Based Reinforcement Learning: R-max + MAXQ
Hierarchical sampling for active learning
I
ICA and ISA Using Schweizer-Wolff Measure of Dependence
Improved Nystrom Low-Rank Approximation and Error Analysis
Inverting the Viterbi Algorithm: An Abstract Framework for Structure Design
K
Knows What It Knows: A Framework For Self-Aware Learning
L
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
Localized Multiple Kernel Learning
Local Likelihood Modeling of Temporal Text Streams
M
Manifold Alignment using Procrustes Analysis
ManifoldBoost: Stagewise Function Approximation for Fully-, Semi- and Un-supervised Learning
Maximum likelihood rule ensembles
Memory Bounded Inference in Topic Models
Metric Embedding for Kernel Classification Rules
Modeling Interleaved Hidden Processes
Modified MMI/MPE: A Direct Evaluation of the Margin in Speech Recognition
MStruct: Inference of Population Structure in Light of Both Genetic Admixing and Allele Mutations
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
N
Nearest Hyperdisk Methods for High-Dimensional Classification
No-Regret Learning in Convex Games
Non-Parametric Policy Gradients: A Unified Treatment of Propositional and Relational Domains
Nonextensive Entropic Kernels
Nonnegative Matrix Factorization via Rank-One Downdate
Nu-Support Vector Machine as Conditional Value-at-Risk Minimization
O
On-line Discovery of Temporal-Difference Networks
Online Kernel Selection for Bayesian Reinforcement Learning
On Multi-View Active Learning and the Combination with Semi-Supervised Learning
On Partial Optimality in Multi-label 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
Optimized Cutting Plane Algorithm for Support Vector Machines
Optimizing Estimated Loss Reduction for Active Sampling in Rank Learning
P
Pairwise Constraint Propagation by Semidefinite Programming for Semi-Supervised Classification
Pointwise exact bootstrap distributions of cost curves
Polyhedral Classifier for Target Detection A Case Study: Colorectal Cancer
Preconditioned Temporal Difference Learning
Predicting Diverse Subsets Using Structural SVMs
Prediction with expert advice for the Brier game
Privacy-Preserving Reinforcement Learning
Q
Query-Level Stability and Generalization in Learning to Rank
R
Random classification noise defeats all convex potential boosters
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-Dependent Kernels
S
Sample-Based Learning and Search with Permanent and Transient Memories
Self-taught Clustering
Sequence Kernels for Predicting Protein Essentiality
Solving graph-structured linear programs: convergence and optimality guarantees
Space-indexed Dynamic Programming: Learning to Follow Trajectories
Sparse Bayesian nonparametric regression
Sparse Multiscale Gaussian Process Regression
Spectral clustering with inconsistent advice
Stability of Transductive Regression Algorithms
Statistical Models for Partial Membership
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
SVM Optimization: Inverse Dependence on Training Set Size
T
Tailoring Density Estimation via Reproducing Kernel Moment Matching
The Asymptotics of Semi-Supervised Learning in Discriminative Probabilistic Models
The Dynamic Hierarchical Dirichlet Process
The GroupLASSO for Generalized Linear Models: uniqueness of solutions and efficient algorithms
The many faces of optimism: a unifying approach
The Projectron: a Bounded Kernel-Based Perceptron
The skew spectrum of graphs
Topologically-Constrained Latent Variable Models
Training Restricted Boltzmann Machines using Approximations to the Likelihood Gradient
Training Structural SVMs when Exact Inference is Intractable
Training SVM with Indefinite Kernels
Transfer of Samples in Batch Reinforcement Learning
U
Uncorrelated Multilinear Principal Component Analysis through Successive Variance Maximization
Unsupervised Rank Aggregation with Distance-Based Models