JMLR · Scour

A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning

Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection

Stable learning using spiking neural networks equipped with affine encoders and decoders

Efficient Knowledge Deletion from Trained Models Through Layer-wise Partial Machine Unlearning

Optimal Complexity in Byzantine-Robust Distributed Stochastic Optimization with Data Heterogeneity

Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation

Collaborative likelihood-ratio estimation over graphs

Towards Understanding Gradient Flow Dynamics of Homogeneous Neural Networks Beyond the Origin

TorchCP: A Python Library for Conformal Prediction

An Asymptotically Optimal Coordinate Descent Algorithm for Learning Bayesian Networks from Gaussian Models

Minimax Optimal Two-Sample Testing under Local Differential Privacy

Jackpot: Approximating Uncertainty Domains with Adversarial Manifolds

Scaling Capability in Token Space: An Analysis of Large Vision Language Model

On the Utility of Equal Batch Sizes for Inference in Stochastic Gradient Descent

Fast Computation of Superquantile-Constrained Optimization Through Implicit Scenario Reduction

Convergence and Sample Complexity of Natural Policy Gradient Primal-Dual Methods for Constrained MDPs

Differentially Private Multivariate Medians

Hopfield-Fenchel-Young Networks: A Unified Framework for Associative Memory Retrieval

Fundamental Limits of Membership Inference Attacks on Machine Learning Models

Identifiability of Causal Graphs under Non-Additive Conditionally Parametric Causal Models

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