39 citations · 148 across the 36 of their papers we have counts for
4 papers · 2 filters
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…
Counter-examples in first-order optimization: a constructive approach
Baptiste Goujaud, Aymeric Dieuleveut, Adrien Taylor
While many approaches were developed for obtaining worst-case complexity bounds for first-order optimization methods in the last years, there remain theoretical gaps in cases where…
Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning
Aymeric Dieuleveut, Gersende Fort, Eric Moulines +1
Stochastic Approximation (SA) is a classical algorithm that has had since the early days a huge impact on signal processing, and nowadays on machine learning, due to the necessity…