arXiv AI
Aug 11
Distributed Optimization with Streaming Data: A Temporal Weighting Perspective
arXiv:2608. 09565v1 Announce Type: cross Abstract: Optimization theory is a widely used tool for intelligent decision-making.
Dangerous Liaisons of Convex Learning and Non-Affine Aggregation
arXiv:2606. 28123v1 Announce Type: new Abstract: Last-iterate convergence and generalization guarantees in first-order convex learning hinge on the monotonicity of the update operator.
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Hugging Face Trending Papers
Jul 13
Enhanced Byzantine-Robust Federated Learning Via Truncated-Quadratic Loss for Heterogeneous Data
Federated learning distributes data among $n$ clients, making it vulnerable to malicious attacks and data heterogeneity, which together pose challenges for robust learning. To tackle this issue, centered clipping and Huber aggregators have been exploited for Byzantine robustness.
arXiv Machine Learning
Jun 5
Efficiently Escaping Saddle Points under Generalized Smoothness via Self-Bounding Regularity
arXiv:2503. 04712v3 Announce Type: replace-cross Abstract: We study the optimization of non-convex functions that are not necessarily smooth (gradient and/or Hessian are Lipschitz) using first order methods.
arXiv Machine Learning
Jul 17
What's in a Smoothness Constant? Tighter Rates for Local SGD with Bounded Second-order Heterogeneity
arXiv:2607. 14731v1 Announce Type: new Abstract: Local SGD, also known as Federated Averaging, is a widely used distributed optimization algorithm.
arXiv Machine Learning
Jul 15
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication
arXiv:2405. 11667v2 Announce Type: replace Abstract: Local SGD is a popular optimization method in distributed learning, often outperforming other algorithms in practice, including mini-batch SGD.
arXiv Machine Learning
Jun 2
Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee
arXiv:2605. 28335v2 Announce Type: replace Abstract: Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but it is highly vulnerable to Byzantine attacks.
arXiv Machine Learning
Jul 14
Enhanced Byzantine-Robust Federated Learning Via Truncated-Quadratic Loss for Heterogeneous Data
arXiv:2607. 10970v1 Announce Type: new Abstract: Federated learning distributes data among $n$ clients, making it vulnerable to malicious attacks and data heterogeneity, which together pose challenges for robust learning.
arXiv Machine Learning
Jul 31
Learning-Augmented and Randomized Algorithms for Line Aggregation with Delays
arXiv:2607. 27807v1 Announce Type: new Abstract: This paper studies learning-augmented and randomized online aggregation with delays on a line metric.
arXiv Machine Learning
Jun 2
Safeguarded Stochastic Polyak Step Sizes for Non-smooth Optimization: Robust Performance Without Small (Sub)Gradients
arXiv:2512. 02342v3 Announce Type: replace-cross Abstract: The stochastic Polyak step size (SPS) has proven to be a promising choice for stochastic gradient descent (SGD), delivering competitive performance relative to state-of-the-art methods on smooth convex and non-convex optimization problems, including deep neural network training.
arXiv Machine Learning
Jun 3
Online Learning with Gradient-Variation Interval Regret
arXiv:2606. 03831v1 Announce Type: new Abstract: This paper investigates non-stationary online learning using the metric of interval regret, which requires an online algorithm to perform well over every time interval.
arXiv Machine Learning
Jul 7
Tight Stability Bounds for Robust Distributed Learning: Byzantine Failures Hurt Generalization More than Data Poisoning
arXiv:2506. 18020v3 Announce Type: replace Abstract: Robust distributed learning algorithms aim to maintain reliable performance despite the presence of misbehaving workers.
arXiv Machine Learning
Jun 5
Learning to optimize with guarantees: a complete characterization of linearly convergent algorithms
arXiv:2508. 00775v2 Announce Type: replace-cross Abstract: The design of many classical optimization algorithms is driven by the certification of linear convergence rates over classes of optimization problems.