Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Skoulakis, Stratis"'
Cutting plane methods are a fundamental approach for solving integer linear programs (ILPs). In each iteration of such methods, additional linear constraints (cuts) are introduced to the constraint set with the aim of excluding the previous fractiona
Externí odkaz:
http://arxiv.org/abs/2406.18781
Given a sequence of functions $f_1,\ldots,f_n$ with $f_i:\mathcal{D}\mapsto \mathbb{R}$, finite-sum minimization seeks a point ${x}^\star \in \mathcal{D}$ minimizing $\sum_{j=1}^n f_j(x)/n$. In this work, we propose a key twist into the finite-sum mi
Externí odkaz:
http://arxiv.org/abs/2406.04731
We present a new algorithm for imitation learning in infinite horizon linear MDPs dubbed ILARL which greatly improves the bound on the number of trajectories that the learner needs to sample from the environment. In particular, we remove exploration
Externí odkaz:
http://arxiv.org/abs/2405.02181
Autor:
Brusca, Lorenzo, Quaedvlieg, Lars C. P. M., Skoulakis, Stratis, Chrysos, Grigorios G, Cevher, Volkan
This work presents a graph neural network (GNN) framework for solving the maximum independent set (MIS) problem, inspired by dynamic programming (DP). Specifically, given a graph, we propose a DP-like recursive algorithm based on GNNs that firstly co
Externí odkaz:
http://arxiv.org/abs/2310.18672
Fictitious Play (FP) is a simple and natural dynamic for repeated play with many applications in game theory and multi-agent reinforcement learning. It was introduced by Brown (1949,1951) and its convergence properties for two-player zero-sum games w
Externí odkaz:
http://arxiv.org/abs/2310.02387
In this work, we introduce a new variant of online gradient descent, which provably converges to Nash Equilibria and simultaneously attains sublinear regret for the class of congestion games in the semi-bandit feedback setting. Our proposed method ad
Externí odkaz:
http://arxiv.org/abs/2306.15543
In this paper we present a first-order method that admits near-optimal convergence rates for convex/concave min-max problems while requiring a simple and intuitive analysis. Similarly to the seminal work of Nemirovski and the recent approach of Pilio
Externí odkaz:
http://arxiv.org/abs/2301.03931
We propose an adaptive variance-reduction method, called AdaSpider, for minimization of $L$-smooth, non-convex functions with a finite-sum structure. In essence, AdaSpider combines an AdaGrad-inspired [Duchi et al., 2011, McMahan & Streeter, 2010], b
Externí odkaz:
http://arxiv.org/abs/2211.01851
STay-ON-the-Ridge: Guaranteed Convergence to Local Minimax Equilibrium in Nonconvex-Nonconcave Games
Min-max optimization problems involving nonconvex-nonconcave objectives have found important applications in adversarial training and other multi-agent learning settings. Yet, no known gradient descent-based method is guaranteed to converge to (even
Externí odkaz:
http://arxiv.org/abs/2210.09769
The study of learning in games has thus far focused primarily on normal form games. In contrast, our understanding of learning in extensive form games (EFGs) and particularly in EFGs with many agents lags far behind, despite them being closer in natu
Externí odkaz:
http://arxiv.org/abs/2207.08426