Learning to identify concise regular expressions that describe email campaigns.
Regularized M-estimators with nonconvexity: statistical and algorithmic theory for local optima.
Divide and conquer kernel ridge regression: a distributed algorithm with minimax optimal rates.
Links between multiplicity automata, observable operator models and predictive state representations: a unified learning framework.
Agnostic learning of disjunctions on symmetric distributions.
The flare package for high dimensional linear regression and precision matrix estimation in R.
Discrete restricted Boltzmann machines.
On the asymptotic normality of an estimate of a regression functional.
Encog: library of interchangeable machine learning models for Java and C#.
On semi-supervised linear regression in covariate shift problems.
Introducing CURRENNT: the munich open-source CUDA recurrent neural network toolkit.
Strong consistency of the prototype based clustering in probabilistic space.
On the inductive bias of dropout.
Non-asymptotic analysis of a new bandit algorithm for semi-bounded rewards.
Combined l1 and greedy l0 penalized least squares for linear model selection.
Fast cross-validation via sequential testing.
Matrix completion and low-rank SVD via fast alternating least squares.
Preface to this special issue.
Geometry and expressive power of conditional restricted Boltzmann machines.
Exceptional rotations of random graphs: a VC theory.
Completing any low-rank matrix, provably.
Learning transformations for clustering and classification.
Perturbed message passing for constraint satisfaction problems.
Distributed matrix completion and robust factorization.
SAMOA: scalable advanced massive online analysis.
Eigenwords: spectral word embeddings.
Global convergence of online limited memory BFGS.
Generalized hierarchical kernel learning.
Multiclass learnability and the ERM principle.
Rationality, optimism and guarantees in general reinforcement learning.
Ultra-scalable and efficient methods for hybrid observational and experimental local causal pathway discovery.
Geometric intuition and algorithms for Ev-SVM.
CEKA: a tool for mining the wisdom of crowds.
Decision boundary for discrete Bayesian network classifiers.
Fast rates in statistical and online learning.
Statistical topological data analysis using persistence landscapes.
Semi-supervised interpolation in an anticausal learning scenario.
Towards an axiomatic approach to hierarchical clustering of measures.
Learning with the maximum correntropy criterion induced losses for regression.
Graphical models via univariate exponential family distributions.
Alexey Chervonenkis's bibliography.
From dependency to causality: a machine learning approach.
Concave penalized estimation of sparse Gaussian Bayesian networks.
The sample complexity of learning linear predictors with the squared loss.
Complexity of equivalence and learning for multiplicity tree automata.
Bayesian nonparametric crowdsourcing.
Learning the structure and parameters of large-population graphical games from behavioral data.
A view of margin losses as regularizers of probability estimates.
Local identification of overcomplete dictionaries.
Optimal Bayesian estimation in random covariate design with a rescaled Gaussian process prior.
pyGPs: a Python library for Gaussian process regression and classification.
Joint estimation of multiple precision matrices with common structures.
Learning using privileged information: similarity control and knowledge transfer.
Learning equilibria of games via payoff queries.
Second-order non-stationary online learning for regression.
Existence and uniqueness of proper scoring rules.
The Randomized Causation Coefficient.
The Libra toolkit for probabilistic models.
The algebraic combinatorial approach for low-rank matrix completion.
Learning sparse low-threshold linear classifiers.
Calibrated multivariate regression with application to neural semantic basis discovery.
Batch learning from logged bandit feedback through counterfactual risk minimization.
Statistical decision making for optimal budget allocation in crowd labeling.
Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm.
A classification module for genetic programming algorithms in JCLEC.
A comprehensive survey on safe reinforcement learning.
A finite sample analysis of the Naive Bayes classifier.
Learning theory of randomized Kaczmarz algorithm.
Marginalizing stacked linear denoising autoencoders.
Online learning via sequential complexities.
A direct estimation of high dimensional stationary vector autoregressions.
SnFFT: a Julia toolkit for Fourier analysis of functions over permutations.
Counting and exploring sizes of Markov equivalence classes of directed acyclic graphs.
RLPy: a value-function-based reinforcement learning framework for education and research.
Network granger causality with inherent grouping structure.
Combination of feature engineering and ranking models for paper-author identification in KDD cup 2013.
Optimality of Poisson processes intensity learning with Gaussian processes.
Absent data generating classifier for imbalanced class sizes.
Derivative estimation based on difference sequence via locally weighted least squares regression.
Multi-layered gesture recognition with Kinect.
Discrete reproducing kernel Hilbert spaces: sampling and distribution of Dirac-masses.
Agnostic insurability of model classes.
Online tensor methods for learning latent variable models.
AD
Adaptive strategy for stratified Monte Carlo sampling.
Photonic delay systems as machine learning implementations.
An asynchronous parallel stochastic coordinate descent algorithm.
Simultaneous pursuit of sparseness and rank structures for matrix decomposition.
When are overcomplete topic models identifiable? uniqueness of tensor tucker decompositions with structured sparsity.
Response-based approachability with applications to generalized no-regret problems.
Alexey Chervonenkis's bibliography: introductory comments.
Minimax analysis of active learning.
Flexible high-dimensional classification machines and their asymptotic properties.
Multimodal gesture recognition via multiple hypotheses rescoring.
PAC optimal MDP planning with application to invasive species management.
A statistical perspective on algorithmic leveraging.
A compression technique for analyzing disagreement-based active learning.
Composite self-concordant minimization.
Plug-and-play dual-tree algorithm runtime analysis.
Evolving GPU machine code.
Condition for perfect dimensionality recovery by variational Bayesian PCA.
Partykit: a modular toolkit for recursive partytioning in R.
Convergence rates for persistence diagram estimation in topological data analysis.
Achievability of asymptotic minimax regret by horizon-dependent and horizon-independent strategies.
Predicting a switching sequence of graph labelings.
Sharp oracle bounds for monotone and convex regression through aggregation.
Bayesian nonparametric covariance regression.
On linearly constrained minimum variance beamforming.
Supervised learning via Euler's Elastica models.
Lasso screening rules via dual polytope projection.
Comparing hard and overlapping clusterings.
Linear dimensionality reduction: survey, insights, and generalizations.
Optimal estimation of low rank density matrices.
Constraint-based causal discovery from multiple interventions over overlapping variable sets.
Iterative and active graph clustering using trace norm minimization without cluster size constraints.
V-matrix method of solving statistical inference problems.
A general framework for fast stagewise algorithms.
Approximate modified policy iteration and its application to the game of Tetris.
