An Exponential Model for Infinite Rankings.
High Dimensional Inverse Covariance Matrix Estimation via Linear Programming.
Classification with Incomplete Data Using Dirichlet Process Priors.
A Convergent Online Single Time Scale Actor Critic Algorithm.
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data.
Semi-Supervised Novelty Detection.
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions.
MOA: Massive Online Analysis.
Efficient Algorithms for Conditional Independence Inference.
Linear Algorithms for Online Multitask Classification.
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion.
Large Scale Online Learning of Image Similarity Through Ranking.
Graph Kernels.
Maximum Relative Margin and Data-Dependent Regularization.
Model Selection: Beyond the Bayesian/Frequentist Divide.
Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes.
Learning Instance-Specific Predictive Models.
Restricted Eigenvalue Properties for Correlated Gaussian Designs.
Online Learning for Matrix Factorization and Sparse Coding.
Fast and Scalable Local Kernel Machines.
Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing.
Tree Decomposition for Large-Scale SVM Problems.
Permutation Tests for Studying Classifier Performance.
FastInf: An Efficient Approximate Inference Library.
Matrix Completion from Noisy Entries.
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design.
Mean Field Variational Approximation for Continuous-Time Bayesian Networks.
Regularized Discriminant Analysis, Ridge Regression and Beyond.
Training and Testing Low-degree Polynomial Data Mappings via Linear SVM.
Near-optimal Regret Bounds for Reinforcement Learning.
Bundle Methods for Regularized Risk Minimization.
On Learning with Integral Operators.
libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models.
Analysis of Multi-stage Convex Relaxation for Sparse Regularization.
A Fast Hybrid Algorithm for Large-Scale
PyBrain.
Second-Order Bilinear Discriminant Analysis.
Covariance in Unsupervised Learning of Probabilistic Grammars.
Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence.
On-Line Sequential Bin Packing.
Sparse Semi-supervised Learning Using Conjugate Functions.
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity.
Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance.
A Generalized Path Integral Control Approach to Reinforcement Learning.
On the Rate of Convergence of the Bagged Nearest Neighbor Estimate.
Sparse Spectrum Gaussian Process Regression.
Quadratic Programming Feature Selection.
Expectation Truncation and the Benefits of Preselection In Training Generative Models.
PAC-Bayesian Analysis of Co-clustering and Beyond.
Hilbert Space Embeddings and Metrics on Probability Measures.
Learnability, Stability and Uniform Convergence.
High-dimensional Variable Selection with Sparse Random Projections: Measurement Sparsity and Statistical Efficiency.
Error-Correcting Ouput Codes Library.
Classification Methods with Reject Option Based on Convex Risk Minimization.
Image Denoising with Kernels Based on Natural Image Relations.
A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning.
Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers.
Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes.
Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting.
The SHOGUN Machine Learning Toolbox.
Lp-Nested Symmetric Distributions.
A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification.
Model-based Boosting 2.0.
Rate Minimaxity of the Lasso and Dantzig Selector for the
Introduction to Causal Inference.
Gaussian Processes for Machine Learning (GPML) Toolbox.
Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization.
On Finding Predictors for Arbitrary Families of Processes.
Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization.
Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models
On the Foundations of Noise-free Selective Classification.
Topology Selection in Graphical Models of Autoregressive Processes.
Learning Translation Invariant Kernels for Classification.
An Efficient Explanation of Individual Classifications using Game Theory.
Approximate Inference on Planar Graphs using Loop Calculus and Belief Propagation.
Using Contextual Representations to Efficiently Learn Context-Free Languages.
Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds.
Composite Binary Losses.
Matched Gene Selection and Committee Classifier for Molecular Classification of Heterogeneous Diseases.
A Rotation Test to Verify Latent Structure.
WEKA - Experiences with a Java Open-Source Project.
On Spectral Learning.
Evolving Static Representations for Task Transfer.
Posterior Regularization for Structured Latent Variable Models.
Regret Bounds and Minimax Policies under Partial Monitoring.
How to Explain Individual Classification Decisions.
Why Does Unsupervised Pre-training Help Deep Learning?
Erratum: SGDQN is Less Careful than Expected.
Consensus-Based Distributed Support Vector Machines.
Continuous Time Bayesian Network Reasoning and Learning Engine.
Optimal Search on Clustered Structural Constraint for Learning Bayesian Network Structure.
Learning From Crowds.
Generalized Power Method for Sparse Principal Component Analysis.
Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels.
Consistent Nonparametric Tests of Independence.
Stochastic Complexity and Generalization Error of a Restricted Boltzmann Machine in Bayesian Estimation.
Kronecker Graphs: An Approach to Modeling Networks.
Importance Sampling for Continuous Time Bayesian Networks.
Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory.
Classification Using Geometric Level Sets.
Stability Bounds for Stationary phi-mixing and beta-mixing Processes.
Rademacher Complexities and Bounding the Excess Risk in Active Learning.
Approximate Tree Kernels.
Practical Approaches to Principal Component Analysis in the Presence of Missing Values.
Inducing Tree-Substitution Grammars.
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation.
Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data.
An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data.
Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes.
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation.
Stochastic Composite Likelihood.
Spectral Regularization Algorithms for Learning Large Incomplete Matrices.
Collective Inference for Extraction MRFs Coupled with Symmetric Clique Potentials.
Learning Non-Stationary Dynamic Bayesian Networks.
Incremental Sigmoid Belief Networks for Grammar Learning.
Characterization, Stability and Convergence of Hierarchical Clustering Methods.
SFO: A Toolbox for Submodular Function Optimization.
A Streaming Parallel Decision Tree Algorithm.
