3 citations · 4 across the 3 of their papers we have counts for
3 papers
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.…
Super-Universal Regularized Newton Method
Nikita Doikov, Konstantin Mishchenko, Yurii Nesterov
We analyze the performance of a variant of Newton method with quadratic regularization for solving composite convex minimization problems. At each step of our method, we choose reg…
Adaptive Learning Rates for Faster Stochastic Gradient Methods
Samuel Horváth, Konstantin Mishchenko, Peter Richtárik
In this work, we propose new adaptive step size strategies that improve several stochastic gradient methods. Our first method (StoPS) is based on the classical Polyak step size (Po…