3.6k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Inception Institute of Artificial IntelligenceAE16 papers
- Carnegie Mellon UniversityUS15 papers
- École PolytechniqueFR11 papers
- Australian National UniversityAU10 papers
- Nankai UniversityCN8 papers
- Centre de Mathématiques Appliquées de l'École polytechniqueFR7 papers
- Nanyang Technological UniversitySG7 papers
- National Research University Higher School of EconomicsRU7 papers
- The University of SydneyAU7 papers
- University of WaterlooCA7 papers
- Hamad bin Khalifa UniversityQA6 papers
- Moscow Institute of Physics and TechnologyRU6 papers
7 papers · 1 filter
Obtaining Lower Query Complexities through Lightweight Zeroth-Order Proximal Gradient Algorithms
Bin Gu, Xiyuan Wei, Hualin Zhang +2
Zeroth-order (ZO) optimization is one key technique for machine learning problems where gradient calculation is expensive or impossible. Several variance reduced ZO proximal algori…
Inexact subgradient methods for semialgebraic functions
Jérôme Bolte, Tam Le, Éric Moulines +1
Motivated by the extensive application of approximate gradients in machine learning and optimization, we investigate inexact subgradient methods subject to persistent additive erro…
Decentralized Optimization Over Slowly Time-Varying Graphs: Algorithms and Lower Bounds
Dmitry Metelev, Aleksandr Beznosikov, Alexander Rogozin +2
We consider a decentralized convex unconstrained optimization problem, where the cost function can be decomposed into a sum of strongly convex and smooth functions, associated with…
Randomized gradient-free methods in convex optimization
Alexander Gasnikov, Darina Dvinskikh, Pavel Dvurechensky +3
This review presents modern gradient-free methods to solve convex optimization problems. By gradient-free methods, we mean those that use only (noisy) realizations of the objective…
Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity
Eduard Gorbunov, Adrien Taylor, Samuel Horváth +1
Algorithms for min-max optimization and variational inequalities are often studied under monotonicity assumptions. Motivated by non-monotone machine learning applications, we follo…
Stochastic Gradient Methods with Preconditioned Updates
Abdurakhmon Sadiev, Aleksandr Beznosikov, Abdulla Jasem Almansoori +3
This work considers the non-convex finite sum minimization problem. There are several algorithms for such problems, but existing methods often work poorly when the problem is badly…
Decentralized Personalized Federated Learning: Lower Bounds and Optimal Algorithm for All Personalization Modes
Abdurakhmon Sadiev, Ekaterina Borodich, Aleksandr Beznosikov +5
This paper considers the problem of decentralized, personalized federated learning. For centralized personalized federated learning, a penalty that measures the deviation from the…