23 citations · 75 across the 31 of their papers we have counts for
11 papers · 1 filter
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…
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…
Open Problem: Two Riddles in Heavy-Ball Dynamics
Baptiste Goujaud, Adrien Taylor, Aymeric Dieuleveut
This short paper presents two open problems on the widely used Polyak's Heavy-Ball algorithm. The first problem is the method's ability to exactly \textit{accelerate} in dimension…
Unified Breakdown Analysis for Byzantine Robust Gossip
Renaud Gaucher, Aymeric Dieuleveut, Hadrien Hendrikx
In decentralized machine learning, different devices communicate in a peer-to-peer manner to collaboratively learn from each other's data. Such approaches are vulnerable to misbeha…
On Fundamental Proof Structures in First-Order Optimization
Baptiste Goujaud, Aymeric Dieuleveut, Adrien Taylor
First-order optimization methods have attracted a lot of attention due to their practical success in many applications, including in machine learning. Obtaining convergence guarant…
Provable non-accelerations of the heavy-ball method
Baptiste Goujaud, Adrien Taylor, Aymeric Dieuleveut
In this work, we show that the heavy-ball ($\HB$) method provably does not reach an accelerated convergence rate on smooth strongly convex problems. More specifically, we show that…