Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations.
State-by-state Minimax Adaptive Estimation for Nonparametric Hidden Markov Models.
Maximum Selection and Sorting with Adversarial Comparators.
A New and Flexible Approach to the Analysis of Paired Comparison Data.
Reverse Iterative Volume Sampling for Linear Regression.
A Constructive Approach to $L_0$ Penalized Regression.
Scalable Bayes via Barycenter in Wasserstein Space.
DALEX: Explainers for Complex Predictive Models in R.
Can We Trust the Bootstrap in High-dimensions? The Case of Linear Models.
On Semiparametric Exponential Family Graphical Models.
Model-Free Trajectory-based Policy Optimization with Monotonic Improvement.
Markov Blanket and Markov Boundary of Multiple Variables.
Robust Synthetic Control.
Clustering is semidefinitely not that hard: Nonnegative SDP for manifold disentangling.
Streaming kernel regression with provably adaptive mean, variance, and regularization.
Goodness-of-Fit Tests for Random Partitions via Symmetric Polynomials.
How Deep Are Deep Gaussian Processes?
Modular Proximal Optimization for Multidimensional Total-Variation Regularization.
A Note on Quickly Sampling a Sparse Matrix with Low Rank Expectation.
Refining the Confidence Level for Optimistic Bandit Strategies.
RSG: Beating Subgradient Method without Smoothness and Strong Convexity.
Statistical Analysis and Parameter Selection for Mapper.
On Generalized Bellman Equations and Temporal-Difference Learning.
Numerical Analysis near Singularities in RBF Networks.
Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems.
Emergence of Invariance and Disentanglement in Deep Representations.
Design and Analysis of the NIPS 2016 Review Process.
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling.
Simple Classification Using Binary Data.
Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery.
Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions.
Learning from Comparisons and Choices.
The xyz algorithm for fast interaction search in high-dimensional data.
A Two-Stage Penalized Least Squares Method for Constructing Large Systems of Structural Equations.
Distribution-Specific Hardness of Learning Neural Networks.
Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models.
Change-Point Computation for Large Graphical Models: A Scalable Algorithm for Gaussian Graphical Models with Change-Points.
A Random Matrix Analysis and Improvement of Semi-Supervised Learning for Large Dimensional Data.
Kernel Density Estimation for Dynamical Systems.
Using Side Information to Reliably Learn Low-Rank Matrices from Missing and Corrupted Observations.
ELFI: Engine for Likelihood-Free Inference.
The Implicit Bias of Gradient Descent on Separable Data.
An efficient distributed learning algorithm based on effective local functional approximations.
Extrapolating Expected Accuracies for Large Multi-Class Problems.
Profile-Based Bandit with Unknown Profiles.
A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms.
Robust PCA by Manifold Optimization.
Optimal Quantum Sample Complexity of Learning Algorithms.
Theoretical Analysis of Cross-Validation for Estimating the Risk of the $k$-Nearest Neighbor Classifier.
Optimal Bounds for Johnson-Lindenstrauss Transformations.
Inference via Low-Dimensional Couplings.
Gradient Descent Learns Linear Dynamical Systems.
Parallelizing Spectrally Regularized Kernel Algorithms.
Dual Principal Component Pursuit.
Accelerating Cross-Validation in Multinomial Logistic Regression with $\ell_1$-Regularization.
Experience Selection in Deep Reinforcement Learning for Control.
Random Forests, Decision Trees, and Categorical Predictors: The "Absent Levels" Problem.
Scaling up Data Augmentation MCMC via Calibration.
Sparse Estimation in Ising Model via Penalized Monte Carlo Methods.
Seglearn: A Python Package for Learning Sequences and Time Series.
An Efficient and Effective Generic Agglomerative Hierarchical Clustering Approach.
A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference.
Importance Sampling for Minibatches.
Invariant Models for Causal Transfer Learning.
OpenEnsembles: A Python Resource for Ensemble Clustering.
Regularized Optimal Transport and the Rot Mover's Distance.
Patchwork Kriging for Large-scale Gaussian Process Regression.
Hinge-Minimax Learner for the Ensemble of Hyperplanes.
Scikit-Multiflow: A Multi-output Streaming Framework.
Multivariate Bayesian Structural Time Series Model.
Improved Asynchronous Parallel Optimization Analysis for Stochastic Incremental Methods.
Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning.
A Robust Learning Approach for Regression Models Based on Distributionally Robust Optimization.
Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters.
Online Bootstrap Confidence Intervals for the Stochastic Gradient Descent Estimator.
Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes.
Approximate Submodularity and its Applications: Subset Selection, Sparse Approximation and Dictionary Selection.
ThunderSVM: A Fast SVM Library on GPUs and CPUs.
Generalized Rank-Breaking: Computational and Statistical Tradeoffs.
Covariances, Robustness, and Variational Bayes.
A Direct Approach for Sparse Quadratic Discriminant Analysis.
Short-term Sparse Portfolio Optimization Based on Alternating Direction Method of Multipliers.
Fast MCMC Sampling Algorithms on Polytopes.
On Tight Bounds for the Lasso.
