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On the Relevance of Byzantine Robust Optimization Against Data Poisoning

2026-06-28 03:19:52

Node Regression on Latent Position Random Graphs via Local Averaging

2026-06-28 03:19:52

Towards Convexity in Anomaly Detection: A New Formulation of SSLM with Unique Optimal Solutions

2026-06-28 03:19:52

Transfer Conformal Predictive Inference for Regression

2026-06-28 03:19:52

Generalized Resubstitution for Regression Error Estimation

2026-06-28 03:19:52

A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equation

2026-06-28 03:19:52

Cheap Bootstrap for Fast Uncertainty Quantification of Stochastic Gradient Descent

2026-06-28 03:19:52

The Role of Contextual Information in Best Arm Identification

2026-06-28 03:19:52

Transfer Learning via Regularized Random-effects Linear Discriminant Analysis

2026-06-28 03:19:52

Inference with non-differentiable surrogate loss in a general high-dimensional classification framework

2026-06-28 03:19:52

Semi-supervised learning for linear extremile regression

2026-06-28 03:19:52

Neural Exploitation and Exploration of Contextual Bandits

2026-06-28 03:19:52

Deep Nonparametric Conditional Independence Tests for Images

2026-06-28 03:19:52

Do We Need to Penalize Variance of Losses for Learning with Label Noise?

2026-06-28 03:19:52

Kernel Mean Embedding Deviation Subspace for Unsupervised Learning with Heterogeneous Data

2026-06-28 03:19:52

Sparse Topic Modeling via Spectral Decomposition and Thresholding

2026-06-28 03:19:52

Exogenous Randomness Empowering Random Forests

2026-06-28 03:19:52

Global Fr{'{e}}chet Manifold Learning for Random Objects, With Application to Low-Dimensional Wasserstein Representations of Distributional Data

2026-06-28 03:19:52

High-dimensional Parameter Transfer With Fused-Regularizer

2026-06-28 03:19:52

Kernel-based Distributed Learning

2026-06-28 03:19:52

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JMLR

On the Relevance of Byzantine Robust Optimization Against Data Poisoning

Node Regression on Latent Position Random Graphs via Local Averaging

Towards Convexity in Anomaly Detection: A New Formulation of SSLM with Unique Optimal Solutions

Transfer Conformal Predictive Inference for Regression

Generalized Resubstitution for Regression Error Estimation

A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equation

Cheap Bootstrap for Fast Uncertainty Quantification of Stochastic Gradient Descent

The Role of Contextual Information in Best Arm Identification

Transfer Learning via Regularized Random-effects Linear Discriminant Analysis

Inference with non-differentiable surrogate loss in a general high-dimensional classification framework

Semi-supervised learning for linear extremile regression

Neural Exploitation and Exploration of Contextual Bandits

Deep Nonparametric Conditional Independence Tests for Images

Do We Need to Penalize Variance of Losses for Learning with Label Noise?

Kernel Mean Embedding Deviation Subspace for Unsupervised Learning with Heterogeneous Data

Sparse Topic Modeling via Spectral Decomposition and Thresholding

Exogenous Randomness Empowering Random Forests

Global Fr{'{e}}chet Manifold Learning for Random Objects, With Application to Low-Dimensional Wasserstein Representations of Distributional Data

High-dimensional Parameter Transfer With Fused-Regularizer

Kernel-based Distributed Learning

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