Learning Transformation Models for Ranking and Survival Analysis.
Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates.
Forest Density Estimation.
Learning Multi-modal Similarity.
Learning a Robust Relevance Model for Search Using Kernel Methods.
Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood.
In All Likelihood, Deep Belief Is Not Enough.
Training SVMs Without Offset.
Models of Cooperative Teaching and Learning.
Unsupervised Supervised Learning II: Margin-Based Classification Without Labels.
The Sample Complexity of Dictionary Learning.
Two Distributed-State Models For Generating High-Dimensional Time Series.
Domain Decomposition Approach for Fast Gaussian Process Regression of Large Spatial Data Sets.
Kernel Regression in the Presence of Correlated Errors.
CARP: Software for Fishing Out Good Clustering Algorithms.
Inverse Reinforcement Learning in Partially Observable Environments.
High-dimensional Covariance Estimation Based On Gaussian Graphical Models.
Computationally Efficient Convolved Multiple Output Gaussian Processes.
Hyper-Sparse Optimal Aggregation.
The Indian Buffet Process: An Introduction and Review.
A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin.
Laplacian Support Vector Machines Trained in the Primal.
Convex and Network Flow Optimization for Structured Sparsity.
Cumulative Distribution Networks and the Derivative-sum-product Algorithm: Models and Inference for Cumulative Distribution Functions on Graphs.
Regression on Fixed-Rank Positive Semidefinite Matrices: A Riemannian Approach.
Robust Approximate Bilinear Programming for Value Function Approximation.
Proximal Methods for Hierarchical Sparse Coding.
Robust Gaussian Process Regression with a Student-
LPmade: Link Prediction Made Easy.
X-Armed Bandits.
A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis.
Efficient and Effective Visual Codebook Generation Using Additive Kernels.
Waffles: A Machine Learning Toolkit.
The Stationary Subspace Analysis Toolbox.
Generalized TD Learning.
Efficient Structure Learning of Bayesian Networks using Constraints.
Parallel Algorithm for Learning Optimal Bayesian Network Structure.
On the Relation between Realizable and Nonrealizable Cases of the Sequence Prediction Problem.
A Simpler Approach to Matrix Completion.
Faster Algorithms for Max-Product Message-Passing.
Theoretical Analysis of Bayesian Matrix Factorization.
Better Algorithms for Benign Bandits.
Stochastic Methods for
Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models.
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.
Introduction to the Special Topic on Grammar Induction, Representation of Language and Language Learning.
Multitask Sparsity via Maximum Entropy Discrimination.
A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes.
Learning from Partial Labels.
Variable Sparsity Kernel Learning.
Logistic Stick-Breaking Process.
On Equivalence Relationships Between Classification and Ranking Algorithms.
Dirichlet Process Mixtures of Generalized Linear Models.
Information, Divergence and Risk for Binary Experiments.
Convergence Rates of Efficient Global Optimization Algorithms.
Structured Variable Selection with Sparsity-Inducing Norms.
Non-Parametric Estimation of Topic Hierarchies from Texts with Hierarchical Dirichlet Processes.
Weisfeiler-Lehman Graph Kernels.
lp-Norm Multiple Kernel Learning.
Information Rates of Nonparametric Gaussian Process Methods.
Minimum Description Length Penalization for Group and Multi-Task Sparse Learning.
Double Updating Online Learning.
Semi-Supervised Learning with Measure Propagation.
Group Lasso Estimation of High-dimensional Covariance Matrices.
A Bayesian Approximation Method for Online Ranking.
Scikit-learn: Machine Learning in Python.
Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation.
Convergence of Distributed Asynchronous Learning Vector Quantization Algorithms.
Multiple Kernel Learning Algorithms.
Approximate Marginals in Latent Gaussian Models.
Hierarchical Knowledge Gradient for Sequential Sampling.
Kernel Analysis of Deep Networks.
Exploiting Best-Match Equations for Efficient Reinforcement Learning.
Neyman-Pearson Classification, Convexity and Stochastic Constraints.
Bayesian Co-Training.
Clustering Algorithms for Chains.
Posterior Sparsity in Unsupervised Dependency Parsing.
Operator Norm Convergence of Spectral Clustering on Level Sets.
Universality, Characteristic Kernels and RKHS Embedding of Measures.
Online Learning in Case of Unbounded Losses Using Follow the Perturbed Leader Algorithm.
Distance Dependent Chinese Restaurant Processes.
Adaptive Exact Inference in Graphical Models.
Improved Moves for Truncated Convex Models.
Smoothness, Disagreement Coefficient, and the Label Complexity of Agnostic Active Learning.
Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data.
Parameter Screening and Optimisation for ILP using Designed Experiments.
Bayesian Generalized Kernel Mixed Models.
DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model.
Anechoic Blind Source Separation Using Wigner Marginals.
Natural Language Processing (Almost) from Scratch.
Locally Defined Principal Curves and Surfaces.
Efficient Learning with Partially Observed Attributes.
MULAN: A Java Library for Multi-Label Learning.
Differentially Private Empirical Risk Minimization.
An Asymptotic Behaviour of the Marginal Likelihood for General Markov Models.
A Family of Simple Non-Parametric Kernel Learning Algorithms.
The arules R-Package Ecosystem: Analyzing Interesting Patterns from Large Transaction Data Sets.
Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation.
Learning with Structured Sparsity.
Large Margin Hierarchical Classification with Mutually Exclusive Class Membership.
MSVMpack: A Multi-Class Support Vector Machine Package.
Union Support Recovery in Multi-task Learning.
Sparse Linear Identifiable Multivariate Modeling.
Learning Latent Tree Graphical Models.
Internal Regret with Partial Monitoring: Calibration-Based Optimal Algorithms.
