Sparse Concordance-assisted Learning for Optimal Treatment Decision.
Averaged Collapsed Variational Bayes Inference.
Certifiably Optimal Low Rank Factor Analysis.
Classification of Time Sequences using Graphs of Temporal Constraints.
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Evolution.
Sharp Oracle Inequalities for Square Root Regularization.
Stochastic Gradient Descent as Approximate Bayesian Inference.
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations.
Nonasymptotic convergence of stochastic proximal point methods for constrained convex optimization.
Clustering with Hidden Markov Model on Variable Blocks.
Maximum Likelihood Estimation for Mixtures of Spherical Gaussians is NP-hard.
Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling.
Kernel Method for Persistence Diagrams via Kernel Embedding and Weight Factor.
Spectral Clustering Based on Local PCA.
Time for a Change: a Tutorial for Comparing Multiple Classifiers Through Bayesian Analysis.
Divide-and-Conquer for Debiased $l_1$-norm Support Vector Machine in Ultra-high Dimensions.
Non-parametric Policy Search with Limited Information Loss.
Bayesian Learning of Dynamic Multilayer Networks.
Compact Convex Projections.
Target Curricula via Selection of Minimum Feature Sets: a Case Study in Boolean Networks.
Memory Efficient Kernel Approximation.
Parallel Symmetric Class Expression Learning.
Enhancing Identification of Causal Effects by Pruning.
Katyusha: The First Direct Acceleration of Stochastic Gradient Methods.
Automatic Differentiation Variational Inference.
Differential Privacy for Bayesian Inference through Posterior Sampling.
Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA.
Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs.
A Theory of Learning with Corrupted Labels.
Sparse Exchangeable Graphs and Their Limits via Graphon Processes.
Distributed Semi-supervised Learning with Kernel Ridge Regression.
Time-Accuracy Tradeoffs in Kernel Prediction: Controlling Prediction Quality.
Submatrix localization via message passing.
Statistical and Computational Guarantees for the Baum-Welch Algorithm.
Consistency, Breakdown Robustness, and Algorithms for Robust Improper Maximum Likelihood Clustering.
Stabilized Sparse Online Learning for Sparse Data.
Identifying Unreliable and Adversarial Workers in Crowdsourced Labeling Tasks.
Regularization and the small-ball method II: complexity dependent error rates.
Gradient Estimation with Simultaneous Perturbation and Compressive Sensing.
A Bayesian Framework for Learning Rule Sets for Interpretable Classification.
Significance-based community detection in weighted networks.
Simplifying Probabilistic Expressions in Causal Inference.
Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization.
On the Propagation of Low-Rate Measurement Error to Subgraph Counts in Large Networks.
Poisson Random Fields for Dynamic Feature Models.
Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network.
pomegranate: Fast and Flexible Probabilistic Modeling in Python.
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization.
An Easy-to-hard Learning Paradigm for Multiple Classes and Multiple Labels.
Computational Limits of A Distributed Algorithm for Smoothing Spline.
Quantifying the Informativeness of Similarity Measurements.
Faithfulness of Probability Distributions and Graphs.
Learning Scalable Deep Kernels with Recurrent Structure.
On the Behavior of Intrinsically High-Dimensional Spaces: Distances, Direct and Reverse Nearest Neighbors, and Hubness.
Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement.
Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits.
A Spectral Algorithm for Inference in Hidden semi-Markov Models.
Simultaneous Clustering and Estimation of Heterogeneous Graphical Models.
Cost-Sensitive Learning with Noisy Labels.
Steering Social Activity: A Stochastic Optimal Control Point Of View.
Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities.
On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models.
Stability of Controllers for Gaussian Process Dynamics.
KELP: a Kernel-based Learning Platform.
Group Sparse Optimization via lp, q Regularization.
Density Estimation in Infinite Dimensional Exponential Families.
In Search of Coherence and Consensus: Measuring the Interpretability of Statistical Topics.
Two New Approaches to Compressed Sensing Exhibiting Both Robust Sparse Recovery and the Grouping Effect.
Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques.
Minimax Estimation of Kernel Mean Embeddings.
Maximum Principle Based Algorithms for Deep Learning.
Reward Maximization Under Uncertainty: Leveraging Side-Observations on Networks.
To Tune or Not to Tune the Number of Trees in Random Forest.
openXBOW - Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit.
On Perturbed Proximal Gradient Algorithms.
Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models.
Convergence Analysis of Distributed Inference with Vector-Valued Gaussian Belief Propagation.
Concentration inequalities for empirical processes of linear time series.
Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks.
A Unified Formulation and Fast Accelerated Proximal Gradient Method for Classification.
Improved spectral community detection in large heterogeneous networks.
Training Gaussian Mixture Models at Scale via Coresets.
Local Identifiability of $\ell_1$-minimization Dictionary Learning: a Sufficient and Almost Necessary Condition.
Regularized Estimation and Testing for High-Dimensional Multi-Block Vector-Autoregressive Models.
On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions.
Efficient Sampling from Time-Varying Log-Concave Distributions.
Convergence of Unregularized Online Learning Algorithms.
SnapVX: A Network-Based Convex Optimization Solver.
Community Detection and Stochastic Block Models: Recent Developments.
Adaptive Randomized Dimension Reduction on Massive Data.
Recovering PCA and Sparse PCA via Hybrid-(l1, l2) Sparse Sampling of Data Elements.
Optimal Rates for Multi-pass Stochastic Gradient Methods.
Learning Partial Policies to Speedup MDP Tree Search via Reduction to I.I.D. Learning.
tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models.
POMDPs.jl: A Framework for Sequential Decision Making under Uncertainty.
Rate of Convergence of $k$-Nearest-Neighbor Classification Rule.
A Bayesian Mixed-Effects Model to Learn Trajectories of Changes from Repeated Manifold-Valued Observations.
Gap Safe Screening Rules for Sparsity Enforcing Penalties.
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization.
Weighted SGD for $\ell_p$ Regression with Randomized Preconditioning.
Persistence Images: A Stable Vector Representation of Persistent Homology.
Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research.
Asymptotic behavior of Support Vector Machine for spiked population model.
Post-Regularization Inference for Time-Varying Nonparanormal Graphical Models.
Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification.
Estimation of Graphical Models through Structured Norm Minimization.
A distributed block coordinate descent method for training l1 regularized linear classifiers.
Achieving Optimal Misclassification Proportion in Stochastic Block Models.
Principled Selection of Hyperparameters in the Latent Dirichlet Allocation Model.
Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA.
Tests of Mutual or Serial Independence of Random Vectors with Applications.
Identifying a Minimal Class of Models for High-dimensional Data.
SGDLibrary: A MATLAB library for stochastic optimization algorithms.
Probabilistic preference learning with the Mallows rank model.
Distributed Learning with Regularized Least Squares.
Lens Depth Function and k-Relative Neighborhood Graph: Versatile Tools for Ordinal Data Analysis.
Improving Variational Methods via Pairwise Linear Response Identities.
Breaking the Curse of Dimensionality with Convex Neural Networks.
Gaussian Lower Bound for the Information Bottleneck Limit.
GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis.
On Markov chain Monte Carlo methods for tall data.
HyperTools: a Python Toolbox for Gaining Geometric Insights into High-Dimensional Data.
On the Stability of Feature Selection Algorithms.
JSAT: Java Statistical Analysis Tool, a Library for Machine Learning.
On Binary Embedding using Circulant Matrices.
Matrix Completion with Noisy Entries and Outliers.
A Tight Bound of Hard Thresholding.
Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging.
Bayesian Inference for Spatio-temporal Spike-and-Slab Priors.
Hierarchical Clustering via Spreading Metrics.
Robust Discriminative Clustering with Sparse Regularizers.
Preference-based Teaching.
Generalized SURE for optimal shrinkage of singular values in low-rank matrix denoising.
On the Consistency of Ordinal Regression Methods.
On $b$-bit Min-wise Hashing for Large-scale Regression and Classification with Sparse Data.
Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers.
Hierarchically Compositional Kernels for Scalable Nonparametric Learning.
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression.
Clustering from General Pairwise Observations with Applications to Time-varying Graphs.
Bridging Supervised Learning and Test-Based Co-optimization.
Learning Certifiably Optimal Rule Lists for Categorical Data.
Nonparametric Risk Bounds for Time-Series Forecasting.
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic.
Simple, Robust and Optimal Ranking from Pairwise Comparisons.
On Faster Convergence of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization.
The Impact of Random Models on Clustering Similarity.
A Survey of Preference-Based Reinforcement Learning Methods.
Deep Learning the Ising Model Near Criticality.
Empirical Evaluation of Resampling Procedures for Optimising SVM Hyperparameters.
Local algorithms for interactive clustering.
A survey of Algorithms and Analysis for Adaptive Online Learning.
An $\ell_{\infty}$ Eigenvector Perturbation Bound and Its Application.
Document Neural Autoregressive Distribution Estimation.
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning.
The Search Problem in Mixture Models.
Provably Correct Algorithms for Matrix Column Subset Selection with Selectively Sampled Data.
Relational Reinforcement Learning for Planning with Exogenous Effects.
Exact Learning of Lightweight Description Logic Ontologies.
Refinery: An Open Source Topic Modeling Web Platform.
Bayesian Tensor Regression.
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding.
STORE: Sparse Tensor Response Regression and Neuroimaging Analysis.
CoCoA: A General Framework for Communication-Efficient Distributed Optimization.
Variational Fourier Features for Gaussian Processes.
Community Extraction in Multilayer Networks with Heterogeneous Community Structure.
Rank Determination for Low-Rank Data Completion.
Bayesian Network Learning via Topological Order.
Angle-based Multicategory Distance-weighted SVM.
Risk-Constrained Reinforcement Learning with Percentile Risk Criteria.
Generalized Pólya Urn for Time-Varying Pitman-Yor Processes.
Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences.
Learning Local Dependence In Ordered Data.
Dimension Estimation Using Random Connection Models.
Multiscale Strategies for Computing Optimal Transport.
Normal Bandits of Unknown Means and Variances.
Soft Margin Support Vector Classification as Buffered Probability Minimization.
Active Nearest-Neighbor Learning in Metric Spaces.
A Nonconvex Approach for Phase Retrieval: Reshaped Wirtinger Flow and Incremental Algorithms.
Interactive Algorithms: Pool, Stream and Precognitive Stream.
Automatic Differentiation in Machine Learning: a Survey.
Characteristic and Universal Tensor Product Kernels.
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation.
Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions.
Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios.
Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server.
Communication-efficient Sparse Regression.
An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback.
Robust and Scalable Bayes via a Median of Subset Posterior Measures.
Gradient Hard Thresholding Pursuit.
Average Stability is Invariant to Data Preconditioning. Implications to Exp-concave Empirical Risk Minimization.
GPflow: A Gaussian Process Library using TensorFlow.
Joint Label Inference in Networks.
Accelerating Stochastic Composition Optimization.
Robust Topological Inference: Distance To a Measure and Kernel Distance.
Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression.
Statistical Inference on Random Dot Product Graphs: a Survey.
Analyzing Tensor Power Method Dynamics in Overcomplete Regime.
Fundamental Conditions for Low-CP-Rank Tensor Completion.
The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems.
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity.
Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice.
Saturating Splines and Feature Selection.
Nearly optimal classification for semimetrics.
Fisher Consistency for Prior Probability Shift.
Making Decision Trees Feasible in Ultrahigh Feature and Label Dimensions.
Optimal Dictionary for Least Squares Representation.
Pycobra: A Python Toolbox for Ensemble Learning and Visualisation.
Active-set Methods for Submodular Minimization Problems.
auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks.
Learning Quadratic Variance Function (QVF) DAG Models via OverDispersion Scoring (ODS).
From Predictive Methods to Missing Data Imputation: An Optimization Approach.
Uncovering Causality from Multivariate Hawkes Integrated Cumulants.
Using Conceptors to Manage Neural Long-Term Memories for Temporal Patterns.
Perishability of Data: Dynamic Pricing under Varying-Coefficient Models.
Second-Order Stochastic Optimization for Machine Learning in Linear Time.
Distributed Sequence Memory of Multidimensional Inputs in Recurrent Networks.
Minimax Filter: Learning to Preserve Privacy from Inference Attacks.
A Robust-Equitable Measure for Feature Ranking and Selection.
Reconstructing Undirected Graphs from Eigenspaces.
A Study of the Classification of Low-Dimensional Data with Supervised Manifold Learning.
Knowledge Graph Completion via Complex Tensor Factorization.
Confidence Sets with Expected Sizes for Multiclass Classification.
Variational Particle Approximations.
The DFS Fused Lasso: Linear-Time Denoising over General Graphs.
Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles.
Online Bayesian Passive-Aggressive Learning.
Approximation Vector Machines for Large-scale Online Learning.
Online Learning to Rank with Top-k Feedback.
Generalized Conditional Gradient for Sparse Estimation.
Asymptotic Analysis of Objectives Based on Fisher Information in Active Learning.
Surprising properties of dropout in deep networks.
Probabilistic Line Searches for Stochastic Optimization.
Kernel Partial Least Squares for Stationary Data.
A Cluster Elastic Net for Multivariate Regression.
