232 citations · 453 across the 44 of their papers we have counts for
7 papers · 1 filter
Valid Stopping in Adaptive Generator-Verifier Loops
Mahmoud Hegazy, Michael I. Jordan, Aymeric Dieuleveut
Numerous agentic workflows are based on a generator-verifier loop: a generator proposes candidates, a cheap verifier scores them, and the workflow terminates when a proposal is ver…
Finding Simple Proofs for First-Order Optimization
Daniel Berg Thomsen, Manu Upadhyaya, Baptiste Goujaud +2
Progress in mathematics often requires more than a certificate of truth: it requires proof structures that are transparent, checkable, and reusable. Automated systems can increasin…
Self-Organized Conformal Prediction: Reducing Regional Coverage Gaps with Unsupervised Group Discovery
Louis Berthier, Ahmed Shokry, Maxime Moreaud +2
Conformal prediction guarantees marginal coverage, but a pooled calibration quantile can hide systematic undercoverage across heterogeneous regions of the feature space. We introdu…
A Tight Theory of Error Feedback Algorithms in Distributed Optimization
Daniel Berg Thomsen, Adrien Taylor, Aymeric Dieuleveut
Communication costs are a major bottleneck in distributed learning and first-order optimization. A common approach to alleviate this issue is to compress the gradient information e…
An optimal first-order method for smooth and strongly convex composite optimization and its stationary limit
Manu Upadhyaya, Daniel Berg Thomsen, Aymeric Dieuleveut +1
We introduce Prox-ITEM, an optimal proximal gradient method for minimizing , where is smooth and strongly convex, and is convex, proper, and lower semicontinuous. In t…
From Inexact Gradients to Byzantine Robustness: Acceleration and Optimization under Similarity
Renaud Gaucher, Aymeric Dieuleveut, Hadrien Hendrikx
Standard federated learning algorithms are vulnerable to adversarial nodes, a.k.a. Byzantine failures. To solve this issue, robust distributed learning algorithms have been develop…
Tight Analysis of Decentralized SGD: A Markov Chain Perspective
Lucas Versini, Paul Mangold, Aymeric Dieuleveut
We propose a novel analysis of the Decentralized Stochastic Gradient Descent (DSGD) algorithm with constant step size, interpreting the iterates of the algorithm as a Markov chain.…