no code implementations • 1 May 2024 • Brad Bachu, Xin Wan, Ciamac C. Moallemi
This work introduces a framework for evaluating onchain order flow auctions (OFAs), emphasizing the metric of price improvement.
no code implementations • 22 Sep 2023 • Davide Crapis, Ciamac C. Moallemi, Shouqiao Wang
We develop a general and practical framework to address the problem of the optimal design of dynamic fee mechanisms for multiple blockchain resources.
no code implementations • 24 May 2023 • Jason Milionis, Ciamac C. Moallemi, Tim Roughgarden
We consider the impact of trading fees on the profits of arbitrageurs trading against an automated marker marker (AMM) or, equivalently, on the adverse selection incurred by liquidity providers due to arbitrage.
no code implementations • 11 Aug 2022 • Jason Milionis, Ciamac C. Moallemi, Tim Roughgarden, Anthony Lee Zhang
Quantitatively, we illustrate how our model's expressions for LP returns match actual LP returns for the Uniswap v2 WETH-USDC trading pair.
no code implementations • 28 Jan 2022 • Seungki Min, Ciamac C. Moallemi, Costis Maglaras
As our problem is a special case of a linear-quadratic-Gaussian control problem with a CVaR objective, these results may be interesting in broader settings.
no code implementations • 30 Jun 2020 • Seungki Min, Ciamac C. Moallemi, Daniel J. Russo
We study the use of policy gradient algorithms to optimize over a class of generalized Thompson sampling policies.
1 code implementation • NeurIPS 2019 • Seungki Min, Costis Maglaras, Ciamac C. Moallemi
With this framework, we define an intuitive family of control policies that include Thompson sampling (TS) and the Bayesian optimal policy as endpoints.
no code implementations • NeurIPS 2012 • Nikhil Bhat, Vivek Farias, Ciamac C. Moallemi
This paper presents a novel non-parametric approximate dynamic programming (ADP) algorithm that enjoys graceful, dimension-independent approximation and sample complexity guarantees.
no code implementations • NeurIPS 2009 • Vijay Desai, Vivek Farias, Ciamac C. Moallemi
We present a novel linear program for the approximation of the dynamic programming cost-to-go function in high-dimensional stochastic control problems.