6 papers · 1 filter
Deep Neural Operator Learning for Probabilistic Models
Erhan Bayraktar, Qi Feng, Zecheng Zhang +1
We propose a deep neural-operator framework for a general class of probability models. Under global Lipschitz conditions on the operator over the entire Euclidean space-and for a b…
On hypoellipticity of degenerate operators in testing and detection problems
Erhan Bayraktar, Yuqiong Wang
We study a class of degenerate diffusion generators arising in sequential testing and quickest detection problems with partial information. The observation process is driven by …
Optimal Matching Strategies in Two-sided Markets: A Mean Field Approach
Erhan Bayraktar, Dantong Chu, Bohan Li +1
This paper develops a mean field game framework for dynamic two-sided matching markets, extending existing matching theory by integrating micro-macro dynamics in two-sided environm…
Dynamic data generation and dynamic portfolio selection: an application of a score-based diffusion model
Ahmad Aghapour, Erhan Bayraktar, Fengyi Yuan
We study dynamic data generation and its application to model-free dynamic portfolio selection. Existing score-based diffusion models are typically designed to learn a static data…
The Learning Approach to Games
Melih İşeri, Erhan Bayraktar
This work introduces a unified framework for analyzing games in greater depth. In the existing literature, players' strategies are typically assigned scalar values, and equilibrium…
Ergodicity and turnpike properties of linear-quadratic mean field control problems
Erhan Bayraktar, Jiamin Jian
We study the asymptotic behavior of solutions to linear-quadratic mean field stochastic optimal control problems. By formulating an ergodic control framework, we characterize the c…