JMLR · Scour

Towards Unified Native Spaces in Kernel Methods

Efficient Online Prediction for High-Dimensional Time Series via Joint Tensor Tucker Decomposition

Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies

On the Robustness of Kernel Goodness-of-Fit Tests

VFOSA: Variance-Reduced Fast Operator Splitting Algorithms for Generalized Equations

Optimizing Data Collection for Machine Learning

Rank-one Convexification for Sparse Regression

Unbalanced Kantorovich-Rubinstein distance, plan, and barycenter on nite spaces: A statistical perspective

Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding

Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning

Efficient and Robust Semi-supervised Estimation of Average Treatment Effect with Partially Annotated Treatment and Response

On the Approximation of Kernel functions

Learning Global Nash Equilibrium in Team Competitive Games with Generalized Fictitious Cross-Play

Wasserstein Convergence Guarantees for a General Class of Score-Based Generative Models

Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization

DAGs as Minimal I-maps for the Induced Models of Causal Bayesian Networks under Conditioning

Manifold Fitting under Unbounded Noise

Extremal graphical modeling with latent variables via convex optimization

Efficient and Robust Transfer Learning of Optimal Individualized Treatment Regimes with Right-Censored Survival Data

The Effect of SGD Batch Size on Autoencoder Learning: Sparsity, Sharpness, and Feature Learning

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