7 papers
MEMOA: Massive Mixtures of Online Agents via Mean-Field Decentralized Nash Equilibria
Xuwei Yang, David B. Emerson, Fatemeh Tavakoli +1
In the modern age of large-scale AI, federated learning has become an increasingly important tool for training large populations of AI agents; however, its computational and commun…
Transformers Can Solve Non-Linear and Non-Markovian Filtering Problems in Continuous Time For Conditionally Gaussian Signals
Blanka Horvath, Anastasis Kratsios, Yannick Limmer +1
The use of attention-based deep learning models in stochastic filtering, e.g. transformers and deep Kalman filters, has recently come into focus; however, the potential for these m…
Simultaneously Solving Infinitely Many LQ Mean Field Games In Hilbert Spaces: The Power of Neural Operators
Dena Firoozi, Anastasis Kratsios, Xuwei Yang
Traditional mean-field game (MFG) solvers operate on an instance-by-instance basis, which becomes infeasible when many related problems must be solved (e.g., for seeking a robust d…
Online Federation For Mixtures of Proprietary Agents with Black-Box Encoders
Xuwei Yang, Fatemeh Tavakoli, David B. Emerson +1
Most industry-standard generative AIs and feature encoders are proprietary, offering only black-box access: their outputs are observable, but their internal parameters and architec…
Higher-Order Transformer Derivative Estimates for Explicit Pathwise Learning Guarantees
Yannick Limmer, Anastasis Kratsios, Xuwei Yang +2
An inherent challenge in computing fully-explicit generalization bounds for transformers involves obtaining covering number estimates for the given transformer class . Crude est…
Neural Operators Can Play Dynamic Stackelberg Games
Guillermo Alvarez, Ibrahim Ekren, Anastasis Kratsios +1
Dynamic Stackelberg games are a broad class of two-player games in which the leader acts first, and the follower chooses a response strategy to the leader's strategy. Unfortunately…