On the mutual nearest neighbors estimate in regression.
Greedy feature selection for subspace clustering.
Optimally fuzzy temporal memory.
Bayesian nonparametric hidden semi-Markov models.
Sparse single-index model.
Beyond Fano's inequality: bounds on the optimal F-score, BER, and cost-sensitive risk and their implications.
Dynamic affine-invariant shape-appearance handshape features and classification in sign language videos.
Quasi-Newton methods: a new direction.
GURLS: a least squares library for supervised learning.
Maximum volume clustering: a new discriminative clustering approach.
How to solve classification and regression problems on high-dimensional data with a supervised extension of slow feature analysis.
Stochastic variational inference.
Sparse/robust estimation and Kalman smoothing with nonsmooth log-concave densities: modeling, computation, and theory.
Finding optimal Bayesian networks using precedence constraints.
Efficient program synthesis using constraint satisfaction in inductive logic programming.
MAGIC summoning: towards automatic suggesting and testing of gestures with low probability of false positives during use.
On the convergence of maximum variance unfolding.
Efficient active learning of halfspaces: an aggressive approach.
Tapkee: an efficient dimension reduction library.
GPstuff: Bayesian modeling with Gaussian processes.
Variational algorithms for marginal MAP.
Approximating the permanent with fractional belief propagation.
Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation.
Using symmetry and evolutionary search to minimize sorting networks.
Cluster analysis: unsupervised learning via supervised learning with a non-convex penalty.
Bayesian Canonical correlation analysis.
Sparse matrix inversion with scaled Lasso.
Message-passing algorithms for quadratic minimization.
A C++ template-based reinforcement learning library: fitting the code to the mathematics.
Multicategory large-margin unified machines.
Machine learning with operational costs.
Sparse activity and sparse connectivity in supervised learning.
Dimension independent similarity computation.
Semi-supervised learning using greedy max-cut.
Construction of approximation spaces for reinforcement learning.
Joint harmonic functions and their supervised connections.
PC algorithm for nonparanormal graphical models.
Learning bilinear model for matching queries and documents.
Nonparametric sparsity and regularization.
The rate of convergence of AdaBoost.
The CAM software for nonnegative blind source separation in R-Java.
Asymptotic results on adaptive false discovery rate controlling procedures based on kernel estimators.
Alleviating naive Bayes attribute independence assumption by attribute weighting.
Orange: data mining toolbox in python.
Parallel vector field embedding.
Random walk kernels and learning curves for Gaussian process regression on random graphs.
A plug-in approach to neyman-pearson classification.
Fast generalized subset scan for anomalous pattern detection.
Belief propagation for continuous state spaces: stochastic message-passing with quantitative guarantees.
Divvy: fast and intuitive exploratory data analysis.
Sub-local constraint-based learning of Bayesian networks using a joint dependence criterion.
Counterfactual reasoning and learning systems: the example of computational advertising.
A near-optimal algorithm for differentially-private principal components.
Multi-stage multi-task feature learning.
Derivative estimation with local polynomial fitting.
On the learnability of shuffle ideals.
MLPACK: a scalable C++ machine learning library.
Language-motivated approaches to action recognition.
A risk comparison of ordinary least squares vs ridge regression.
A widely applicable Bayesian information criterion.
Truncated power method for sparse eigenvalue problems.
Distribution-dependent sample complexity of large margin learning.
Convex and scalable weakly labeled SVMs.
A max-norm constrained minimization approach to 1-bit matrix completion.
Distributions of angles in random packing on spheres.
Variational inference in nonconjugate models.
Large-scale SVD and manifold learning.
Optimal discovery with probabilistic expert advice: finite time analysis and macroscopic optimality.
Classifying with confidence from incomplete information.
Manifold regularization and semi-supervised learning: some theoretical analyses.
Sparsity regret bounds for individual sequences in online linear regression.
CODA: high dimensional copula discriminant analysis.
Consistent selection of tuning parameters via variable selection stability.
Gaussian Kullback-Leibler approximate inference.
Supervised feature selection in graphs with path coding penalties and network flows.
Variable selection in high-dimension with random designs and orthogonal matching pursuit.
Global analytic solution of fully-observed variational Bayesian matrix factorization.
Similarity-based clustering by left-stochastic matrix factorization.
Algorithms and hardness results for parallel large margin learning.
Distance preserving embeddings for general n-dimensional manifolds.
Lower bounds and selectivity of weak-consistent policies in stochastic multi-armed bandit problem.
Training energy-based models for time-series imputation.
Query induction with schema-guided pruning strategies.
Nested expectation propagation for Gaussian process classification.
Experiment selection for causal discovery.
Greedy sparsity-constrained optimization.
Perturbative corrections for approximate inference in Gaussian latent variable models.
Risk bounds of learning processes for Lévy processes.
Comment on "Robustness and regularization of support vector machines" by H. Xu et al. (Journal of machine learning research, volume 10, pp 1485-1510, 2009).
Fast MCMC sampling for Markov jump processes and extensions.
Segregating event streams and noise with a Markov renewal process model.
One-shot learning gesture recognition from RGB-D data using bag of features.
Stochastic dual coordinate ascent methods for regularized loss.
Stress functions for nonlinear dimension reduction, proximity analysis, and graph drawing.
Learning theory analysis for association rules and sequential event prediction.
Learning trees from strings: a strong learning algorithm for some context-free grammars.
JKernelMachines: a simple framework for kernel machine.
A binary-classification-based metric between time-series distributions and its use in statistical and learning problems.
A framework for evaluating approximation methods for Gaussian process regression.
Algorithms for discovery of multiple Markov boundaries.
Kernel Bayes' rule: Bayesian inference with positive definite kernels.
Keep it simple and sparse: real-time action recognition.
A theory of multiclass boosting.
Lovász ϑ function, SVMs and finding dense subgraphs.
Multivariate convex regression with adaptive partitioning.
Random spanning trees and the prediction ofweighted graphs.
Differential privacy for functions and functional data.
Learning theory approach to minimum error entropy criterion.
Ranked bandits in metric spaces: learning diverse rankings over large document collections.
QuantMiner for mining quantitative association rules.
Improving CUR matrix decomposition and the Nyström approximation via adaptive sampling.
Ranking forests.
Communication-efficient algorithms for statistical optimization.
Stationary-sparse causality network learning.
BudgetedSVM: a toolbox for scalable SVM approximations.
Conjugate relation between loss functions and uncertainty sets in classification problems.
Regularization-free principal curve estimation.
Performance bounds for λ policy iteration and application to the game of Tetris.
Classifier selection using the predicate depth.
Pairwise likelihood ratios for estimation of non-Gaussian structural equation models.
Universal consistency of localized versions of regularized kernel methods.
