LLORMA: Local Low-Rank Matrix Approximation.
Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision.
Convex Regression with Interpretable Sharp Partitions.
Feature-Level Domain Adaptation.
An Error Bound for L1-norm Support Vector Machine Coefficients in Ultra-high Dimension.
A General Framework for Constrained Bayesian Optimization using Information-based Search.
MLlib: Machine Learning in Apache Spark.
Blending Learning and Inference in Conditional Random Fields.
Machine Learning in an Auction Environment.
Equivalence of Graphical Lasso and Thresholding for Sparse Graphs.
Penalized Maximum Likelihood Estimation of Multi-layered Gaussian Graphical Models.
Distributed Coordinate Descent Method for Learning with Big Data.
On Lower and Upper Bounds in Smooth and Strongly Convex Optimization.
On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models.
Operator-valued Kernels for Learning from Functional Response Data.
On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm.
Refined Error Bounds for Several Learning Algorithms.
Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm.
The Teaching Dimension of Linear Learners.
Choice of V for V-Fold Cross-Validation in Least-Squares Density Estimation.
Compressed Gaussian Process for Manifold Regression.
Volumetric Spanners: An Efficient Exploration Basis for Learning.
Extremal Mechanisms for Local Differential Privacy.
Multivariate Spearman's rho for Aggregating Ranks Using Copulas.
Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches.
Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing.
Mutual Information Based Matching for Causal Inference with Observational Data.
Multiple Output Regression with Latent Noise.
Multi-Objective Markov Decision Processes for Data-Driven Decision Support.
Minimax Adaptive Estimation of Nonparametric Hidden Markov Models.
Learning Taxonomy Adaptation in Large-scale Classification.
Bootstrap-Based Regularization for Low-Rank Matrix Estimation.
Nonparametric Network Models for Link Prediction.
Bayesian group factor analysis with structured sparsity.
On the Characterization of a Class of Fisher-Consistent Loss Functions and its Application to Boosting.
Noisy Sparse Subspace Clustering.
CVXPY: A Python-Embedded Modeling Language for Convex Optimization.
GenSVM: A Generalized Multiclass Support Vector Machine.
Subspace Learning with Partial Information.
Distribution-Matching Embedding for Visual Domain Adaptation.
OLPS: A Toolbox for On-Line Portfolio Selection.
Improving Structure MCMC for Bayesian Networks through Markov Blanket Resampling.
Composite Multiclass Losses.
Analysis of Classification-based Policy Iteration Algorithms.
A Well-Conditioned and Sparse Estimation of Covariance and Inverse Covariance Matrices Using a Joint Penalty.
Lenient Learning in Independent-Learner Stochastic Cooperative Games.
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models.
Gains and Losses are Fundamentally Different in Regret Minimization: The Sparse Case.
Exact Inference on Gaussian Graphical Models of Arbitrary Topology using Path-Sums.
Input Output Kernel Regression: Supervised and Semi-Supervised Structured Output Prediction with Operator-Valued Kernels.
Learning Theory for Distribution Regression.
Latent Space Inference of Internet-Scale Networks.
Kernel Mean Shrinkage Estimators.
A Network That Learns Strassen Multiplication.
The Statistical Performance of Collaborative Inference.
Augmentable Gamma Belief Networks.
Consistent Algorithms for Clustering Time Series.
Optimal Learning Rates for Localized SVMs.
Random Rotation Ensembles.
Newton-Stein Method: An Optimization Method for GLMs via Stein's Lemma.
Learning Latent Variable Models by Pairwise Cluster Comparison: Part II - Algorithm and Evaluation.
Guarding against Spurious Discoveries in High Dimensions.
CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data.
Trend Filtering on Graphs.
The Optimal Sample Complexity of PAC Learning.
Convergence of an Alternating Maximization Procedure.
Convex Calibration Dimension for Multiclass Loss Matrices.
Regularized Policy Iteration with Nonparametric Function Spaces.
A General Framework for Consistency of Principal Component Analysis.
Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics.
Approximate Newton Methods for Policy Search in Markov Decision Processes.
Universal Approximation Results for the Temporal Restricted Boltzmann Machine and the Recurrent Temporal Restricted Boltzmann Machine.
DSA: Decentralized Double Stochastic Averaging Gradient Algorithm.
Integrated Common Sense Learning and Planning in POMDPs.
Multiscale Adaptive Representation of Signals: I. The Basic Framework.
Classification of Imbalanced Data with a Geometric Digraph Family.
MOCCA: Mirrored Convex/Concave Optimization for Nonconvex Composite Functions.
Conditional Independencies under the Algorithmic Independence of Conditionals.
Synergy of Monotonic Rules.
Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence.
Challenges in multimodal gesture recognition.
Consistent Distribution-Free $K$-Sample and Independence Tests for Univariate Random Variables.
Optimal Estimation of Derivatives in Nonparametric Regression.
Probabilistic Low-Rank Matrix Completion from Quantized Measurements.
Linear Convergence of Randomized Feasible Descent Methods Under the Weak Strong Convexity Assumption.
Stable Graphical Models.
Scalable Learning of Bayesian Network Classifiers.
Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes.
Practical Kernel-Based Reinforcement Learning.
Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices.
One-class classification of point patterns of extremes.
A Bounded p-norm Approximation of Max-Convolution for Sub-Quadratic Bayesian Inference on Additive Factors.
Distributed Submodular Maximization.
L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs.
On the Influence of Momentum Acceleration on Online Learning.
Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes.
Learning Planar Ising Models.
A Consistent Information Criterion for Support Vector Machines in Diverging Model Spaces.
Multi-Task Learning for Straggler Avoiding Predictive Job Scheduling.
Jointly Informative Feature Selection Made Tractable by Gaussian Modeling.
Complexity of Representation and Inference in Compositional Models with Part Sharing.
A Characterization of Linkage-Based Hierarchical Clustering.
Domain-Adversarial Training of Neural Networks.
Model-free Variable Selection in Reproducing Kernel Hilbert Space.
BayesPy: Variational Bayesian Inference in Python.
String and Membrane Gaussian Processes.
Learning Algorithms for Second-Price Auctions with Reserve.
Decrypting "Cryptogenic" Epilepsy: Semi-supervised Hierarchical Conditional Random Fields For Detecting Cortical Lesions In MRI-Negative Patients.
Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing.
Low-Rank Doubly Stochastic Matrix Decomposition for Cluster Analysis.
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle.
On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching.
RLScore: Regularized Least-Squares Learners.
Weak Convergence Properties of Constrained Emphatic Temporal-difference Learning with Constant and Slowly Diminishing Stepsize.
Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach.
Consistency of Cheeger and Ratio Graph Cuts.
New Perspectives on k-Support and Cluster Norms.
Large Scale Online Kernel Learning.
Neyman-Pearson Classification under High-Dimensional Settings.
Measuring Dependence Powerfully and Equitably.
Kernel Estimation and Model Combination in A Bandit Problem with Covariates.
Covariance-based Clustering in Multivariate and Functional Data Analysis.
Optimal Estimation and Completion of Matrices with Biclustering Structures.
A New Algorithm and Theory for Penalized Regression-based Clustering.
Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning.
A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees.
The Asymptotic Performance of Linear Echo State Neural Networks.
Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders.
Are Random Forests Truly the Best Classifiers?
Should We Really Use Post-Hoc Tests Based on Mean-Ranks?
Multiscale Dictionary Learning: Non-Asymptotic Bounds and Robustness.
Multi-task Sparse Structure Learning with Gaussian Copula Models.
Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms.
JCLAL: A Java Framework for Active Learning.
Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes.
Estimating Causal Structure Using Conditional DAG Models.
Sparse PCA via Covariance Thresholding.
On Quantile Regression in Reproducing Kernel Hilbert Spaces with the Data Sparsity Constraint.
Learning Latent Variable Models by Pairwise Cluster Comparison: Part I - Theory and Overview.
Minimax Rates in Permutation Estimation for Feature Matching.
Joint Structural Estimation of Multiple Graphical Models.
Local Network Community Detection with Continuous Optimization of Conductance and Weighted Kernel K-Means.
Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Learn Neural Networks.
Learning Using Anti-Training with Sacrificial Data.
A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares.
The Constrained Dantzig Selector with Enhanced Consistency.
mlr: Machine Learning in R.
A Gibbs Sampler for Learning DAGs.
Variational Dependent Multi-output Gaussian Process Dynamical Systems.
Scalable Approximate Bayesian Inference for Outlier Detection under Informative Sampling.
Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics.
How to Center Deep Boltzmann Machines.
Multiple-Instance Learning from Distributions.
Data-driven Rank Breaking for Efficient Rank Aggregation.
On the properties of variational approximations of Gibbs posteriors.
Integrative Analysis using Coupled Latent Variable Models for Individualizing Prognoses.
Loss Minimization and Parameter Estimation with Heavy Tails.
Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares.
Differentially Private Data Releasing for Smooth Queries.
Efficient Computation of Gaussian Process Regression for Large Spatial Data Sets by Patching Local Gaussian Processes.
Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation.
Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing.
On Bayes Risk Lower Bounds.
Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition.
SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions.
Large Scale Visual Recognition through Adaptation using Joint Representation and Multiple Instance Learning.
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights.
Neural Autoregressive Distribution Estimation.
Revisiting the Nystrom Method for Improved Large-scale Machine Learning.
ERRATA: On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm.
Structure-Leveraged Methods in Breast Cancer Risk Prediction.
Control Function Instrumental Variable Estimation of Nonlinear Causal Effect Models.
A Variational Approach to Path Estimation and Parameter Inference of Hidden Diffusion Processes.
Harry: A Tool for Measuring String Similarity.
e-PAL: An Active Learning Approach to the Multi-Objective Optimization Problem.
Semiparametric Mean Field Variational Bayes: General Principles and Numerical Issues.
Hierarchical Relative Entropy Policy Search.
Stability and Generalization in Structured Prediction.
The Benefit of Multitask Representation Learning.
Knowledge Matters: Importance of Prior Information for Optimization.
Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models.
Addressing Environment Non-Stationarity by Repeating Q-learning Updates.
Online Trans-dimensional von Mises-Fisher Mixture Models for User Profiles.
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels.
Monotonic Calibrated Interpolated Look-Up Tables.
Adaptive Lasso and group-Lasso for functional Poisson regression.
Rate Optimal Denoising of Simultaneously Sparse and Low Rank Matrices.
Scaling-up Empirical Risk Minimization: Optimization of Incomplete $U$-statistics.
Causal Inference through a Witness Protection Program.
The LRP Toolbox for Artificial Neural Networks.
A Unified View on Multi-class Support Vector Classification.
An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning.
Bipartite Ranking: a Risk-Theoretic Perspective.
Bayesian Policy Gradient and Actor-Critic Algorithms.
A Closer Look at Adaptive Regret.
Cells in Multidimensional Recurrent Neural Networks.
StructED: Risk Minimization in Structured Prediction.
Characteristic Kernels and Infinitely Divisible Distributions.
Bayesian Graphical Models for Multivariate Functional Data.
Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests.
Importance Weighting Without Importance Weights: An Efficient Algorithm for Combinatorial Semi-Bandits.
Modelling Interactions in High-dimensional Data with Backtracking.
Gradients Weights improve Regression and Classification.
Herded Gibbs Sampling.
Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling.
Rounding-based Moves for Semi-Metric Labeling.
Electronic Health Record Analysis via Deep Poisson Factor Models.
Bounding the Search Space for Global Optimization of Neural Networks Learning Error: An Interval Analysis Approach.
Iterative Regularization for Learning with Convex Loss Functions.
fastFM: A Library for Factorization Machines.
Non-linear Causal Inference using Gaussianity Measures.
Online PCA with Optimal Regret.
A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning.
Multiplicative Multitask Feature Learning.
Learning the Variance of the Reward-To-Go.
bandicoot: a Python Toolbox for Mobile Phone Metadata.
Megaman: Scalable Manifold Learning in Python.
Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs.
Structure Discovery in Bayesian Networks by Sampling Partial Orders.
LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems.
Fused Lasso Approach in Regression Coefficients Clustering - Learning Parameter Heterogeneity in Data Integration.
Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation.
MEKA: A Multi-label/Multi-target Extension to WEKA.
End-to-End Training of Deep Visuomotor Policies.
Minimum Density Hyperplanes.
Multi-scale Classification using Localized Spatial Depth.
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks.
A Note on the Sample Complexity of the Er-SpUD Algorithm by Spielman, Wang and Wright for Exact Recovery of Sparsely Used Dictionaries.
The Factorized Self-Controlled Case Series Method: An Approach for Estimating the Effects of Many Drugs on Many Outcomes.
Spectral Ranking using Seriation.
An Information-Theoretic Analysis of Thompson Sampling.
Adjusting for Chance Clustering Comparison Measures.
An Online Convex Optimization Approach to Blackwell's Approachability.
True Online Temporal-Difference Learning.
Dual Control for Approximate Bayesian Reinforcement Learning.
Wavelet decompositions of Random Forests - smoothness analysis, sparse approximation and applications.
