Time-to-Event Prediction with Neural Networks and Cox Regression.
Matched Bipartite Block Model with Covariates.
Minimal Sample Subspace Learning: Theory and Algorithms.
Transport Analysis of Infinitely Deep Neural Network.
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction.
Shared Subspace Models for Multi-Group Covariance Estimation.
Multi-scale Online Learning: Theory and Applications to Online Auctions and Pricing.
Kernel Approximation Methods for Speech Recognition.
Dynamic Pricing in High-dimensions.
Iterated Learning in Dynamic Social Networks.
Best Arm Identification for Contaminated Bandits.
Train and Test Tightness of LP Relaxations in Structured Prediction.
Gaussian Processes with Linear Operator Inequality Constraints.
Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets.
Adaptation Based on Generalized Discrepancy.
Deep Reinforcement Learning for Swarm Systems.
Efficient augmentation and relaxation learning for individualized treatment rules using observational data.
A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization.
GraSPy: Graph Statistics in Python.
Analysis of spectral clustering algorithms for community detection: the general bipartite setting.
The Common-directions Method for Regularized Empirical Risk Minimization.
Lazifying Conditional Gradient Algorithms.
Convergence Guarantees for a Class of Non-convex and Non-smooth Optimization Problems.
SMART: An Open Source Data Labeling Platform for Supervised Learning.
Kernels for Sequentially Ordered Data.
Variance-based Regularization with Convex Objectives.
Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery.
Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression.
Characterizing the Sample Complexity of Pure Private Learners.
spark-crowd: A Spark Package for Learning from Crowdsourced Big Data.
Spurious Valleys in One-hidden-layer Neural Network Optimization Landscapes.
Approximation Algorithms for Stochastic Clustering.
Generalized Maximum Entropy Estimation.
Tunability: Importance of Hyperparameters of Machine Learning Algorithms.
Semi-Analytic Resampling in Lasso.
Generalized Score Matching for Non-Negative Data.
Learning Optimized Risk Scores.
Model Selection in Bayesian Neural Networks via Horseshoe Priors.
Deep Optimal Stopping.
Model Selection via the VC Dimension.
Parsimonious Online Learning with Kernels via Sparse Projections in Function Space.
On the Convergence of Gaussian Belief Propagation with Nodes of Arbitrary Size.
Learnability of Solutions to Conjunctive Queries.
Collective Matrix Completion.
Generic Inference in Latent Gaussian Process Models.
scikit-multilearn: A Python library for Multi-Label Classification.
Streaming Principal Component Analysis From Incomplete Data.
Nonparametric Estimation of Probability Density Functions of Random Persistence Diagrams.
Utilizing Second Order Information in Minibatch Stochastic Variance Reduced Proximal Iterations.
Learning to Match via Inverse Optimal Transport.
DBSCAN: Optimal Rates For Density-Based Cluster Estimation.
Binarsity: a penalization for one-hot encoded features in linear supervised learning.
ORCA: A Matlab/Octave Toolbox for Ordinal Regression.
Determining the Number of Latent Factors in Statistical Multi-Relational Learning.
Complete Search for Feature Selection in Decision Trees.
Provably Accurate Double-Sparse Coding.
Nonuniformity of P-values Can Occur Early in Diverging Dimensions.
All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously.
Quantification Under Prior Probability Shift: the Ratio Estimator and its Extensions.
Neural Empirical Bayes.
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion.
Why do deep convolutional networks generalize so poorly to small image transformations?
Low Permutation-rank Matrices: Structural Properties and Noisy Completion.
Decentralized Dictionary Learning Over Time-Varying Digraphs.
The Relationship Between Agnostic Selective Classification, Active Learning and the Disagreement Coefficient.
Bayesian Combination of Probabilistic Classifiers using Multivariate Normal Mixtures.
Convergence Rate of a Simulated Annealing Algorithm with Noisy Observations.
Scaling Up Sparse Support Vector Machines by Simultaneous Feature and Sample Reduction.
Graph Reduction with Spectral and Cut Guarantees.
Solving the OSCAR and SLOPE Models Using a Semismooth Newton-Based Augmented Lagrangian Method.
Causal Learning via Manifold Regularization.
Log-concave sampling: Metropolis-Hastings algorithms are fast.
DataWig: Missing Value Imputation for Tables.
Adaptive Geometric Multiscale Approximations for Intrinsically Low-dimensional Data.
Multiplicative local linear hazard estimation and best one-sided cross-validation.
Pyro: Deep Universal Probabilistic Programming.
TensorLy: Tensor Learning in Python.
Proximal Distance Algorithms: Theory and Practice.
SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition.
The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks.
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals.
Maximum Likelihood for Gaussian Process Classification and Generalized Linear Mixed Models under Case-Control Sampling.
Deep Exploration via Randomized Value Functions.
NetSDM: Semantic Data Mining with Network Analysis.
An Efficient Two Step Algorithm for High Dimensional Change Point Regression Models Without Grid Search.
Scalable Approximations for Generalized Linear Problems.
Fast Automatic Smoothing for Generalized Additive Models.
Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning.
Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA.
AffectiveTweets: a Weka Package for Analyzing Affect in Tweets.
iNNvestigate Neural Networks!
Forward-Backward Selection with Early Dropping.
Learning Attribute Patterns in High-Dimensional Structured Latent Attribute Models.
DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization.
An asymptotic analysis of distributed nonparametric methods.
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning.
Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis.
Measuring the Effects of Data Parallelism on Neural Network Training.
Optimal Policies for Observing Time Series and Related Restless Bandit Problems.
Scalable Interpretable Multi-Response Regression via SEED.
Exact Clustering of Weighted Graphs via Semidefinite Programming.
A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication.
Spectrum Estimation from a Few Entries.
Simultaneous Phase Retrieval and Blind Deconvolution via Convex Programming.
Robust Frequent Directions with Application in Online Learning.
Stochastic Variance-Reduced Cubic Regularization Methods.
The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising.
Smooth neighborhood recommender systems.
Learning Overcomplete, Low Coherence Dictionaries with Linear Inference.
A Representer Theorem for Deep Kernel Learning.
On Asymptotic and Finite-Time Optimality of Bayesian Predictors.
Multi-class Heterogeneous Domain Adaptation.
Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions.
Differentiable Game Mechanics.
Non-Convex Matrix Completion and Related Problems via Strong Duality.
A Representer Theorem for Deep Neural Networks.
Approximations of the Restless Bandit Problem.
Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python.
Thompson Sampling Guided Stochastic Searching on the Line for Deceptive Environments with Applications to Root-Finding Problems.
Bayesian Space-Time Partitioning by Sampling and Pruning Spanning Trees.
Bayesian Optimization for Policy Search via Online-Offline Experimentation.
Robust Estimation of Derivatives Using Locally Weighted Least Absolute Deviation Regression.
Approximation Hardness for A Class of Sparse Optimization Problems.
Logical Explanations for Deep Relational Machines Using Relevance Information.
Distributed Inference for Linear Support Vector Machine.
Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations.
Stochastic Canonical Correlation Analysis.
Fairness Constraints: A Flexible Approach for Fair Classification.
Determinantal Point Processes for Coresets.
Analysis of Langevin Monte Carlo via Convex Optimization.
Automated Scalable Bayesian Inference via Hilbert Coresets.
DPPy: DPP Sampling with Python.
Delay and Cooperation in Nonstochastic Bandits.
Nonparametric Bayesian Aggregation for Massive Data.
Joint PLDA for Simultaneous Modeling of Two Factors.
Ivanov-Regularised Least-Squares Estimators over Large RKHSs and Their Interpolation Spaces.
ADMMBO: Bayesian Optimization with Unknown Constraints using ADMM.
An Approach to One-Bit Compressed Sensing Based on Probably Approximately Correct Learning Theory.
PyOD: A Python Toolbox for Scalable Outlier Detection.
Approximate Profile Maximum Likelihood.
Prediction Risk for the Horseshoe Regression.
Hamiltonian Monte Carlo with Energy Conserving Subsampling.
Monotone Learning with Rectified Wire Networks.
No-Regret Bayesian Optimization with Unknown Hyperparameters.
Sparse Kernel Regression with Coefficient-based $\ell_q-$regularization.
A Well-Tempered Landscape for Non-convex Robust Subspace Recovery.
Decoupling Sparsity and Smoothness in the Dirichlet Variational Autoencoder Topic Model.
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise.
Accelerated Alternating Projections for Robust Principal Component Analysis.
Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics.
High-Dimensional Poisson Structural Equation Model Learning via $\ell_1$-Regularized Regression.
Unsupervised Evaluation and Weighted Aggregation of Ranked Classification Predictions.
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices.
Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots.
Differentiable reservoir computing.
Scalable Kernel K-Means Clustering with Nystr\"om Approximation: Relative-Error Bounds.
Unsupervised Basis Function Adaptation for Reinforcement Learning.
Simultaneous Private Learning of Multiple Concepts.
Neural Architecture Search: A Survey.
Optimal Convergence Rates for Convex Distributed Optimization in Networks.
A Kernel Multiple Change-point Algorithm via Model Selection.
Dependent relevance determination for smooth and structured sparse regression.
Learning Representations of Persistence Barcodes.
Embarrassingly Parallel Inference for Gaussian Processes.
Active Learning for Cost-Sensitive Classification.
Learning Unfaithful $K$-separable Gaussian Graphical Models.
Convergence of Gaussian Belief Propagation Under General Pairwise Factorization: Connecting Gaussian MRF with Pairwise Linear Gaussian Model.
Decontamination of Mutual Contamination Models.
Nearly-tight VC-dimension and Pseudodimension Bounds for Piecewise Linear Neural Networks.
Regularization via Mass Transportation.
Layer-Wise Learning Strategy for Nonparametric Tensor Product Smoothing Spline Regression and Graphical Models.
On the optimality of the Hedge algorithm in the stochastic regime.
Learning by Unsupervised Nonlinear Diffusion.
Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping.
Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations.
A New Approach to Laplacian Solvers and Flow Problems.
Tight Lower Bounds on the VC-dimension of Geometric Set Systems.
Quantifying Uncertainty in Online Regression Forests.
High-dimensional Varying Index Coefficient Models via Stein's Identity.
More Efficient Estimation for Logistic Regression with Optimal Subsamples.
On Consistent Vertex Nomination Schemes.
Multiclass Boosting: Margins, Codewords, Losses, and Algorithms.
New Convergence Aspects of Stochastic Gradient Algorithms.
Optimal Transport: Fast Probabilistic Approximation with Exact Solvers.
