Multi-instance learning with any hypothesis class.
Multi-target regression with rule ensembles.
Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso.
Coherence functions with applications in large-margin classification methods.
Stability of density-based clustering.
Analysis of a Random Forests Model.
Sampling Methods for the Nyström Method.
A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models.
ML-Flex: A Flexible Toolbox for Performing Classification Analyses In Parallel.
Local and global scaling reduce hubs in space.
Regularized bundle methods for convex and non-convex risks.
Finite-sample analysis of least-squares policy iteration.
Multi Kernel Learning with Online-Batch Optimization.
Random Search for Hyper-Parameter Optimization.
Online Learning in the Embedded Manifold of Low-rank Matrices.
Non-Sparse Multiple Kernel Fisher Discriminant Analysis.
Fast approximation of matrix coherence and statistical leverage.
Sign language recognition using sub-units.
Algebraic Geometric Comparison of Probability Distributions.
Mal-ID: Automatic Malware Detection Using Common Segment Analysis and Meta-Features.
Integrating a Partial Model into Model Free Reinforcement Learning.
Quantum set intersection and its application to associative memory.
NIMFA: A Python Library for Nonnegative Matrix Factorization.
Active Learning via Perfect Selective Classification.
Query Strategies for Evading Convex-Inducing Classifiers.
Multi-task regression using minimal penalties.
Learning Algorithms for the Classification Restricted Boltzmann Machine.
An Active Learning Algorithm for Ranking from Pairwise Preferences with an Almost Optimal Query Complexity.
Linear fitted-Q iteration with multiple reward functions.
An introduction to artificial prediction markets for classification.
A Multi-Stage Framework for Dantzig Selector and LASSO.
Active Clustering of Biological Sequences.
Selective sampling and active learning from single and multiple teachers.
A Comparison of the Lasso and Marginal Regression.
Positive Semidefinite Metric Learning Using Boosting-like Algorithms.
Consistent Model Selection Criteria on High Dimensions.
Pattern for Python.
A Case Study on Meta-Generalising: A Gaussian Processes Approach.
An Improved GLMNET for L1-regularized Logistic Regression.
Online submodular minimization.
Nonparametric guidance of autoencoder representations using label information.
Metric and Kernel Learning Using a Linear Transformation.
Regularization Techniques for Learning with Matrices.
SVDFeature: a toolkit for feature-based collaborative filtering.
Human gesture recognition on product manifolds.
MULTIBOOST: A Multi-purpose Boosting Package.
Hope and Fear for Discriminative Training of Statistical Translation Models.
Multi-Assignment Clustering for Boolean Data.
Trading regret for efficiency: online convex optimization with long term constraints.
A Kernel Two-Sample Test.
DARWIN: a framework for machine learning and computer vision research and development.
Linear regression with random projections.
Jstacs: A Java Framework for Statistical Analysis and Classification of Biological Sequences.
Restricted Strong Convexity and Weighted Matrix Completion: Optimal Bounds with Noise.
Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications.
Large-scale linear support vector regression.
Smoothing multivariate performance measures.
The huge Package for High-dimensional Undirected Graph Estimation in R.
Pairwise support vector machines and their application to large scale problems.
Distance Metric Learning with Eigenvalue Optimization.
Transfer in Reinforcement Learning via Shared Features.
Oger: modular learning architectures for large-scale sequential processing.
Optimistic Bayesian Sampling in Contextual-Bandit Problems.
Facilitating score and causal inference trees for large observational studies.
Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection.
Mixability is Bayes Risk Curvature Relative to Log Loss.
Refinement of Operator-valued Reproducing Kernels.
Activized Learning: Transforming Passive to Active with Improved Label Complexity.
Variable Selection in High-dimensional Varying-coefficient Models with Global Optimality.
Structured Sparsity and Generalization.
Bayesian mixed-effects inference on classification performance in hierarchical data sets.
Algorithms for Learning Kernels Based on Centered Alignment.
Optimal Distributed Online Prediction Using Mini-Batches.
High-dimensional Gaussian graphical model selection: walk summability and local separation criterion.
Discriminative hierarchical part-based models for human parsing and action recognition.
Iterative reweighted algorithms for matrix rank minimization.
Breaking the curse of kernelization: budgeted stochastic gradient descent for large-scale SVM training.
Causal Bounds and Observable Constraints for Non-deterministic Models.
Eliminating Spammers and Ranking Annotators for Crowdsourced Labeling Tasks.
Static prediction games for adversarial learning problems.
Exploration in relational domains for model-based reinforcement learning.
MedLDA: maximum margin supervised topic models.
On the convergence rate of lp-norm multiple kernel learning.
Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming.
EP-GIG Priors and Applications in Bayesian Sparse Learning.
Security analysis of online centroid anomaly detection.
On Ranking and Generalization Bounds.
Feature Selection via Dependence Maximization.
Sally: a tool for embedding strings in vector spaces.
A local spectral method for graphs: with applications to improving graph partitions and exploring data graphs locally.
Efficient methods for robust classification under uncertainty in kernel matrices.
On the necessity of irrelevant variables.
Learning linear cyclic causal models with latent variables.
Manifold Identification in Dual Averaging for Regularized Stochastic Online Learning.
Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs.
PREA: personalized recommendation algorithms toolkit.
GPLP: A Local and Parallel Computation Toolbox for Gaussian Process Regression.
glm-ie: Generalised Linear Models Inference & Estimation Toolbox.
Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics.
Confidence-Weighted Linear Classification for Text Categorization.
Learning symbolic representations of hybrid dynamical systems.
Towards Integrative Causal Analysis of Heterogeneous Data Sets and Studies.
A Primal-Dual Convergence Analysis of Boosting.
Plug-in Approach to Active Learning.
Finding recurrent patterns from continuous sign language sentences for automated extraction of signs.
Sparse and unique nonnegative matrix factorization through data preprocessing.
Bounding the Probability of Error for High Precision Optical Character Recognition.
A unified view of performance metrics: translating threshold choice into expected classification loss.
DEAP: evolutionary algorithms made easy.
Entropy Search for Information-Efficient Global Optimization.
Minimax Manifold Estimation.
Dynamic policy programming.
A topic modeling toolbox using belief propagation.
Robust kernel density estimation.
Variational Multinomial Logit Gaussian Process.
Structured Sparsity via Alternating Direction Methods.
A Geometric Approach to Sample Compression.
PAC-bayes bounds with data dependent priors.
A Model of the Perception of Facial Expressions of Emotion by Humans: Research Overview and Perspectives.
