5 papers
Importance Sampling for Event Discovery via Guesswork
Asaf Cohen
Traditional importance sampling (IS) is designed to estimate rare-event probabilities by minimizing estimator variance. However, many applications prioritize rapid discovery: the g…
Operator Learning for Families of Finite-State Mean-Field Games
William Hofgard, Asaf Cohen, Mathieu Laurière
Finite-state mean-field games (MFGs) arise as limits of large interacting particle systems and are governed by an MFG system, a coupled forward-backward differential equation consi…
Uniform-in-Time Convergence Rates to a Nonlinear Markov Chain for Mean-Field Interacting Jump Processes
Asaf Cohen, Ethan Huffman
We consider a system of particles interacting through their empirical distribution on a finite state space in continuous time. In the formal limit as , the system t…
DEX Specs: A Mean Field Approach to DeFi Currency Exchanges
Erhan Bayraktar, Asaf Cohen, April Nellis
We investigate the behavior of liquidity providers (LPs) by modeling a decentralized cryptocurrency exchange (DEX) based on Uniswap v3. LPs with heterogeneous characteristics choos…
Learning a faceted customer segmentation for discovering new business opportunities at Intel
Itay Lieder, Meirav Segal, Eran Avidan +2
For sales and marketing organizations within large enterprises, identifying and understanding new markets, customers and partners is a key challenge. Intel's Sales and Marketing Gr…