Search Results for author: Stratis Skoulakis

Found 7 papers, 1 papers with code

Optimal No-Regret Learning in General Games: Bounded Regret with Unbounded Step-Sizes via Clairvoyant MWU

no code implementations29 Nov 2021 Georgios Piliouras, Ryann Sim, Stratis Skoulakis

We shift away from this paradigm by defining a novel algorithm that we call Clairvoyant Multiplicative Weights Updates (CMWU).

Online Learning in Periodic Zero-Sum Games

no code implementations NeurIPS 2021 Tanner Fiez, Ryann Sim, Stratis Skoulakis, Georgios Piliouras, Lillian Ratliff

Classical learning results build on this theorem to show that online no-regret dynamics converge to an equilibrium in a time-average sense in zero-sum games.

Efficient Online Learning for Dynamic k-Clustering

no code implementations8 Jun 2021 Dimitris Fotakis, Georgios Piliouras, Stratis Skoulakis

We study dynamic clustering problems from the perspective of online learning.

Evolutionary Game Theory Squared: Evolving Agents in Endogenously Evolving Zero-Sum Games

no code implementations15 Dec 2020 Stratis Skoulakis, Tanner Fiez, Ryann Sim, Georgios Piliouras, Lillian Ratliff

The predominant paradigm in evolutionary game theory and more generally online learning in games is based on a clear distinction between a population of dynamic agents that interact given a fixed, static game.

Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent

1 code implementation NeurIPS 2020 Dimitris Fotakis, Thanasis Lianeas, Georgios Piliouras, Stratis Skoulakis

We consider a natural model of online preference aggregation, where sets of preferred items $R_1, R_2, \ldots, R_t$ along with a demand for $k_t$ items in each $R_t$, appear online.

Dimensionality Reduction

The Complexity of Constrained Min-Max Optimization

no code implementations21 Sep 2020 Constantinos Daskalakis, Stratis Skoulakis, Manolis Zampetakis

In this paper, we provide a characterization of the computational complexity of the problem, as well as of the limitations of first-order methods in constrained min-max optimization problems with nonconvex-nonconcave objectives and linear constraints.

Convergence to Second-Order Stationarity for Non-negative Matrix Factorization: Provably and Concurrently

no code implementations26 Feb 2020 Ioannis Panageas, Stratis Skoulakis, Antonios Varvitsiotis, Xiao Wang

Non-negative matrix factorization (NMF) is a fundamental non-convex optimization problem with numerous applications in Machine Learning (music analysis, document clustering, speech-source separation etc).

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