101 citations · 488 across the 33 of their papers we have counts for
7 papers · 1 filter
Adaptive Proximal Gradient Method for Convex Optimization
Yura Malitsky, Konstantin Mishchenko
In this paper, we explore two fundamental first-order algorithms in convex optimization, namely, gradient descent (GD) and proximal gradient method (ProxGD). Our focus is on making…
Prodigy: An Expeditiously Adaptive Parameter-Free Learner
Konstantin Mishchenko, Aaron Defazio
We consider the problem of estimating the learning rate in adaptive methods, such as AdaGrad and Adam. We propose Prodigy, an algorithm that provably estimates the distance to the…
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity
Konstantin Mishchenko, Rustem Islamov, Eduard Gorbunov +1
We present a partially personalized formulation of Federated Learning (FL) that strikes a balance between the flexibility of personalization and cooperativeness of global training.…
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method
Ahmed Khaled, Konstantin Mishchenko, Chi Jin
This paper proposes a new easy-to-implement parameter-free gradient-based optimizer: DoWG (Distance over Weighted Gradients). We prove that DoWG is efficient -- matching the conver…
Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy
Blake Woodworth, Konstantin Mishchenko, Francis Bach
We present an algorithm for minimizing an objective with hard-to-compute gradients by using a related, easier-to-access function as a proxy. Our algorithm is based on approximate p…
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes
Konstantin Mishchenko, Slavomír Hanzely, Peter Richtárik
In this work, we consider the problem of minimizing the sum of Moreau envelopes of given functions, which has previously appeared in the context of meta-learning and personalized f…
Learning-Rate-Free Learning by D-Adaptation
Aaron Defazio, Konstantin Mishchenko
D-Adaptation is an approach to automatically setting the learning rate which asymptotically achieves the optimal rate of convergence for minimizing convex Lipschitz functions, with…