5 citations · 11 across the 9 of their papers we have counts for
9 papers
Accelerated regularized learning in finite N-person games
Kyriakos Lotidis, Angeliki Giannou, Panayotis Mertikopoulos +1
Motivated by the success of Nesterov's accelerated gradient algorithm for convex minimization problems, we examine whether it is possible to achieve similar performance gains in th…
Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition
Zheyang Xiong, Ziyang Cai, John Cooper +11
Large Language Models (LLMs) have demonstrated remarkable in-context learning (ICL) capabilities. In this study, we explore a surprising phenomenon related to ICL: LLMs can perform…
How Well Can Transformers Emulate In-context Newton's Method?
Angeliki Giannou, Liu Yang, Tianhao Wang +2
Transformer-based models have demonstrated remarkable in-context learning capabilities, prompting extensive research into its underlying mechanisms. Recent studies have suggested t…
Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements
Emmanouil-Vasileios Vlatakis-Gkaragkounis, Angeliki Giannou, Yudong Chen +1
For min-max optimization and variational inequalities problems (VIP) encountered in diverse machine learning tasks, Stochastic Extragradient (SEG) and Stochastic Gradient Descent A…
Dissecting Chain-of-Thought: Compositionality through In-Context Filtering and Learning
Yingcong Li, Kartik Sreenivasan, Angeliki Giannou +2
Chain-of-thought (CoT) is a method that enables language models to handle complex reasoning tasks by decomposing them into simpler steps. Despite its success, the underlying mechan…
The Expressive Power of Tuning Only the Normalization Layers
Angeliki Giannou, Shashank Rajput, Dimitris Papailiopoulos
Feature normalization transforms such as Batch and Layer-Normalization have become indispensable ingredients of state-of-the-art deep neural networks. Recent studies on fine-tuning…
Looped Transformers as Programmable Computers
Angeliki Giannou, Shashank Rajput, Jy-yong Sohn +3
We present a framework for using transformer networks as universal computers by programming them with specific weights and placing them in a loop. Our input sequence acts as a punc…
On the convergence of policy gradient methods to Nash equilibria in general stochastic games
Angeliki Giannou, Kyriakos Lotidis, Panayotis Mertikopoulos +1
Learning in stochastic games is a notoriously difficult problem because, in addition to each other's strategic decisions, the players must also contend with the fact that the game…
Survival of the strictest: Stable and unstable equilibria under regularized learning with partial information
Angeliki Giannou, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Panayotis Mertikopoulos
In this paper, we examine the Nash equilibrium convergence properties of no-regret learning in general N-player games. For concreteness, we focus on the archetypal follow the regul…