activity
20202022
most citedAdaptive extra-gradient methods for min-max optimization and games

8 citations · 11 across the 4 of their papers we have counts for

collaborators

6 papers

math.OC20221 cited

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

Ali Kavis, Stratis Skoulakis, Kimon Antonakopoulos +2

We propose an adaptive variance-reduction method, called AdaSpider, for minimization of -smooth, non-convex functions with a finite-sum structure. In essence, AdaSpider combines…

cs.GT2022

Routing in an Uncertain World: Adaptivity, Efficiency, and Equilibrium

Dong Quan Vu, Kimon Antonakopoulos, Panayotis Mertikopoulos

We consider the traffic assignment problem in nonatomic routing games where the players' cost functions may be subject to random fluctuations (e.g., weather disturbances, perturbat…

math.OC20212 cited

Adaptive first-order methods revisited: Convex optimization without Lipschitz requirements

Kimon Antonakopoulos, Panayotis Mertikopoulos

We propose a new family of adaptive first-order methods for a class of convex minimization problems that may fail to be Lipschitz continuous or smooth in the standard sense. Specif…

cs.GT2021

Adaptive Learning in Continuous Games: Optimal Regret Bounds and Convergence to Nash Equilibrium

Yu-Guan Hsieh, Kimon Antonakopoulos, Panayotis Mertikopoulos

In game-theoretic learning, several agents are simultaneously following their individual interests, so the environment is non-stationary from each player's perspective. In this con…

cs.LG2021

On the Generalization of Stochastic Gradient Descent with Momentum

Ali Ramezani-Kebrya, Ashish Khisti, Ben Liang

While momentum-based methods, in conjunction with stochastic gradient descent (SGD), are widely used when training machine learning models, there is little theoretical understandin…

math.OC20208 cited

Adaptive extra-gradient methods for min-max optimization and games

Kimon Antonakopoulos, E. Veronica Belmega, Panayotis Mertikopoulos

We present a new family of min-max optimization algorithms that automatically exploit the geometry of the gradient data observed at earlier iterations to perform more informative e…