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Found 7 papers, 1 papers with code
How Well Can Transformers Emulate In-context Newton's Method?
Recent studies have suggested that Transformers can implement first-order optimization algorithms for in-context learning and even second order ones for the case of linear regression.
Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements
Our work endeavors to elucidate and quantify the probabilistic structures intrinsic to these algorithms.
The Expressive Power of Tuning Only the Normalization Layers
Feature normalization transforms such as Batch and Layer-Normalization have become indispensable ingredients of state-of-the-art deep neural networks.
Looped Transformers as Programmable Computers
We present a framework for using transformer networks as universal computers by programming them with specific weights and placing them in a loop.
On the convergence of policy gradient methods to Nash equilibria in general stochastic games
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 itself evolves over time, possibly in a very complicated manner.
On the Rate of Convergence of Regularized Learning in Games: From Bandits and Uncertainty to Optimism and Beyond
In this paper, we examine the convergence rate of a wide range of regularized methods for learning in games.
Survival of the strictest: Stable and unstable equilibria under regularized learning with partial information
This equivalence extends existing continuous-time versions of the folk theorem of evolutionary game theory to a bona fide algorithmic learning setting, and it provides a clear refinement criterion for the prediction of the day-to-day behavior of no-regret learning in games
