arXiv Machine Learning
Learning-Theoretic Foundation for General Coded Computing: The Adversarial Setting
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arXiv Machine Learning
Sep 1
Learning-Theoretic Foundation for General Coded Computing: The Straggler Setting
arXiv:2608.28910v1 Announce Type: new Abstract: Coded computing has emerged as a powerful paradigm for mitigating the impact of straggling workers in distributed computing systems. However, existing...
arXiv Machine Learning
Aug 11
Convex Basins in Single-Index Model Loss Landscapes: Applications to Robust Recovery under Strong Adversarial Corruption
arXiv:2605. 29497v2 Announce Type: replace Abstract: We study the problem of robustly learning Gaussian Single Index Models (SIMs) in the presence of heavy-tailed noise and a constant fraction of adversarially corrupted covariates and responses.
arXiv Machine Learning
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Adversarial Training with Large Step Sizes: Implicit Bias, Optimization, and Sharpness
The paper investigates how deterministic Gradient Descent behaves when applied to Adversarial Training with large, constant step sizes. It analyzes both a linear logistic regression setting—examining where parameters converge (implicit bias) and how fast losses decrease (optimization)—and neural network training, focusing on the evolution of loss sharpness. The study reveals three key distinctions from Standard Training: the implicit bias gap persists even with large steps, there is no uniform convergence rate for optimization error, and sharpness follows a two‑stage dynamic unlike the Edge‑of‑Stability pattern seen in Standard Training.
arXiv Machine Learning
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Byzantine-Robust Federated Representation Learning
arXiv:2609.36660v1 Announce Type: new Abstract: We study federated learning (FL) with adversarial clients, where the goal is to minimize the average loss of the honest (non-adversarial) clients witho...
arXiv Machine Learning
Aug 28
Adversarial Training Without Input Gradients via Low-Rank Householder Expansions
The paper introduces a method for adversarial training that avoids computing input gradients by using a low‑rank Householder expansion (LRHE) to directly generate small‑norm adversarial examples from a network’s parameters. This approach requires only forward passes and standard back‑propagation, eliminating the inner maximization loop and reducing computational cost to roughly 2.8 PGD steps per epoch. The resulting models achieve comparable robustness to multi‑step PGD training for small relative ε budgets, demonstrating the feasibility of gradient‑free adversarial training.
arXiv Machine Learning
Jun 30
Solver-Integrated Adversarial Attacking and Training of Neural Operators
arXiv:2510. 18989v2 Announce Type: replace Abstract: Neural operators are commonly utilized as fast surrogates for numerical solvers in PDE problems, mapping input functions to solution functions.