NDLI: Exploration scavenging

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Gaussian process product models for nonparametric nonstationarity

Sequence kernels for predicting protein essentiality

Hierarchical kernel stick-breaking process for multi-task image analysis

Graph kernels between point clouds

Bolasso: model consistent Lasso estimation through the bootstrap

Learning all optimal policies with multiple criteria

Multiple instance ranking

Multi-task learning for HIV therapy screening

Nonnegative matrix factorization via rank-one downdate

Strategy evaluation in extensive games with importance sampling

Actively learning level-sets of composite functions

Sparse Bayesian nonparametric regression

An empirical evaluation of supervised learning in high dimensions

Fast support vector machine training and classification on graphics processors

Fast nearest neighbor retrieval for bregman divergences

Nearest hyperdisk methods for high-dimensional classification

Learning to sportscast: a test of grounded language acquisition

Training SVM with indefinite kernels

Learning for control from multiple demonstrations

Spectral clustering with inconsistent advice

A unified architecture for natural language processing: deep neural networks with multitask learning

Autonomous geometric precision error estimation in low-level computer vision tasks

Stability of transductive regression algorithms

A rate-distortion one-class model and its applications to clustering

Fast Gaussian process methods for point process intensity estimation

Self-taught clustering

Hierarchical sampling for active learning

Learning to classify with missing and corrupted features

Maximum likelihood rule ensembles

Learning from incomplete data with infinite imputations

An object-oriented representation for efficient reinforcement learning

Optimizing estimated loss reduction for active sampling in rank learning

Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs

Confidence-weighted linear classification

Efficient projections onto the $l_{1}-ball$ for learning in high dimensions

Pointwise exact bootstrap distributions of cost curves

Polyhedral classifier for target detection: a case study: colorectal cancer

Active reinforcement learning

Training structural SVMs when exact inference is intractable

An HDP-HMM for systems with state persistence

Optimized cutting plane algorithm for support vector machines

Stopping conditions for exact computation of leave-one-out error in support vector machines

Reinforcement learning in the presence of rare events

Memory bounded inference in topic models

Localized multiple kernel learning

No-regret learning in convex games

Boosting with incomplete information

Grassmann discriminant analysis: a unifying view on subspace-based learning

Modified MMI/MPE: a direct evaluation of the margin in speech recognition

Statistical models for partial membership

Active kernel learning

A dual coordinate descent method for large-scale linear SVM

Discriminative structure and parameter learning for Markov logic networks

Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity

Hierarchical model-based reinforcement learning: R-max + MAXQ

Efficient bandit algorithms for online multiclass prediction

Large scale manifold transduction

Non-parametric policy gradients: a unified treatment of propositional and relational domains

ICA and ISA using Schweizer-Wolff measure of dependence

Unsupervised rank aggregation with distance-based models

On partial optimality in multi-label MRFs

Space-indexed dynamic programming: learning to follow trajectories

The skew spectrum of graphs

Fast estimation of first-order clause coverage through randomization and maximum likelihood

Query-level stability and generalization in learning to rank

Modeling interleaved hidden processes

Exploration scavenging

Classification using discriminative restricted Boltzmann machines

Transfer of samples in batch reinforcement learning

Local likelihood modeling of temporal text streams

A worst-case comparison between temporal difference and residual gradient with linear function approximation

Knows what it knows: a framework for self-aware learning

Pairwise constraint propagation by semidefinite programming for semi-supervised classification

An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators

Structure compilation: trading structure for features

ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning

Random classification noise defeats all convex potential boosters

Uncorrelated multilinear principal component analysis through successive variance maximization

A reproducing kernel Hilbert space framework for pairwise time series distances

On-line discovery of temporal-difference networks

Nonextensive entropic kernels

Automatic discovery and transfer of MAXQ hierarchies

Rank minimization via online learning

An analysis of reinforcement learning with function approximation

Empirical Bernstein stopping

Efficiently solving convex relaxations for MAP estimation

On the hardness of finding symmetries in Markov decision processes

Bayes optimal classification for decision trees

A decoupled approach to exemplar-based unsupervised learning

Cost-sensitive multi-class classification from probability estimates

The projectron: a bounded kernel-based Perceptron

Learning dissimilarities by ranking: from SDP to QP

A distance model for rhythms

On the chance accuracies of large collections of classifiers

An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning

Learning to learn implicit queries from gaze patterns

Multi-task compressive sensing with Dirichlet process priors

Estimating labels from label proportions

Learning diverse rankings with multi-armed bandits

Semi-supervised learning of compact document representations with deep networks

Message-passing for graph-structured linear programs: proximal projections, convergence and rounding schemes

Bayesian multiple instance learning: automatic feature selection and inductive transfer

Online kernel selection for Bayesian reinforcement learning

The dynamic hierarchical Dirichlet process

Closed-form supervised dimensionality reduction with generalized linear models

Bi-level path following for cross validated solution of kernel quantile regression

The Group-Lasso for generalized linear models: uniqueness of solutions and efficient algorithms

Robust matching and recognition using context-dependent kernels

Privacy-preserving reinforcement learning

On the quantitative analysis of deep belief networks

Bayesian probabilistic matrix factorization using Markov chain Monte Carlo

Accurate max-margin training for structured output spaces

Fast incremental proximity search in large graphs

Inverting the Viterbi algorithm: an abstract framework for structure design

Compressed sensing and Bayesian experimental design

Multi-classification by categorical features via clustering

SVM optimization: inverse dependence on training set size

Data spectroscopy: learning mixture models using eigenspaces of convolution operators

A generalization of Haussler's convolution kernel: mapping kernel

mStruct: a new admixture model for inference of population structure in light of both genetic admixing and allele mutations

Expectation-maximization for sparse and non-negative PCA

Sample-based learning and search with permanent and transient memories

An RKHS for multi-view learning and manifold co-regularization

The asymptotics of semi-supervised learning in discriminative probabilistic models

Tailoring density estimation via reproducing kernel moment matching

Detecting statistical interactions with additive groves of trees

Metric embedding for kernel classification rules

Discriminative parameter learning for Bayesian networks

A least squares formulation for canonical correlation analysis

Apprenticeship learning using linear programming

Composite kernel learning

The many faces of optimism: a unifying approach

ν-support vector machine as conditional value-at-risk minimization

Training restricted Boltzmann machines using approximations to the likelihood gradient

A semiparametric statistical approach to model-free policy evaluation

Topologically-constrained latent variable models

Beam sampling for the infinite hidden Markov model

Extracting and composing robust features with denoising autoencoders

Prediction with expert advice for the Brier game

Sparse multiscale gaussian process regression

Manifold alignment using Procrustes analysis

Dirichlet component analysis: feature extraction for compositional data

Adaptive p-posterior mixture-model kernels for multiple instance learning

Graph transduction via alternating minimization

On multi-view active learning and the combination with semi-supervised learning

Fast solvers and efficient implementations for distance metric learning

Deep learning via semi-supervised embedding

Efficiently learning linear-linear exponential family predictive representations of state

Fully distributed EM for very large datasets

Listwise approach to learning to rank: theory and algorithm

Democratic approximation of lexicographic preference models

Preconditioned temporal difference learning

A quasi-Newton approach to non-smooth convex optimization

Predicting diverse subsets using structural SVMs

Improved Nyström low-rank approximation and error analysis

Estimating local optimums in EM algorithm over Gaussian mixture model

Efficient multiclass maximum margin clustering

Laplace maximum margin Markov networks

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