71 citations · 151 across the 51 of their papers we have counts for
8 papers · 1 filter
Asynchronous Optimization over Graphs: Linear Convergence under Error Bound Conditions
Loris Cannelli, Francisco Facchinei, Gesualdo Scutari +1
We consider convex and nonconvex constrained optimization with a partially separable objective function: agents minimize the sum of local objective functions, each of which is know…
Stochastic Gradient Langevin with Delayed Gradients
Vyacheslav Kungurtsev, Bapi Chatterjee, Dan Alistarh
Stochastic Gradient Langevin Dynamics (SGLD) ensures strong guarantees with regards to convergence in measure for sampling log-concave posterior distributions by adding noise to st…
Reinforcement Learning Based on Real-Time Iteration NMPC
Mario Zanon, Vyacheslav Kungurtsev, Sébastien Gros
Reinforcement Learning (RL) has proven a stunning ability to learn optimal policies from data without any prior knowledge on the process. The main drawback of RL is that it is typi…
Convergence and Complexity Analysis of a Levenberg-Marquardt Algorithm for Inverse Problems
E. Bergou, Y. Diouane, V. Kungurtsev
The Levenberg-Marquardt algorithm is one of the most popular algorithms for finding the solution of nonlinear least squares problems. Across different modified variations of the ba…
Complexity iteration analysis for strongly convex multi-objective optimization using a Newton path-following procedure
E. Bergou, Y. Diouane, V. Kungurtsev
In this note we consider the iteration complexity of solving strongly convex multi objective optimization. We discuss the precise meaning of this problem, and indicate it is loosel…
Zero Order Stochastic Weakly Convex Composite Optimization
V. Kungurtsev, F. Rinaldi
In this paper we consider stochastic weakly convex composite problems, however without the existence of a stochastic subgradient oracle. We present a derivative free algorithm that…
Elastic Consistency: A General Consistency Model for Distributed Stochastic Gradient Descent
Giorgi Nadiradze, Ilia Markov, Bapi Chatterjee +2
Machine learning has made tremendous progress in recent years, with models matching or even surpassing humans on a series of specialized tasks. One key element behind the progress…
Decentralized Langevin Dynamics
Vyacheslav Kungurtsev
Langevin MCMC gradient optimization is a class of increasingly popular methods for estimating a posterior distribution. This paper addresses the algorithm as applied in a decentral…