35 citations · 225 across the 65 of their papers we have counts for
9 papers · 1 filter
Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe
Renbo Zhao, Niccolò Dalmasso, Mohsen Ghassemi +3
Hawkes processes have recently risen to the forefront of tools when it comes to modeling and generating sequential events data. Multidimensional Hawkes processes model both the sel…
Optimal Stopping with Gaussian Processes
Kshama Dwarakanath, Danial Dervovic, Peyman Tavallali +2
We propose a novel group of Gaussian Process based algorithms for fast approximate optimal stopping of time series with specific applications to financial markets. We show that str…
Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations
Nelson Vadori, Leo Ardon, Sumitra Ganesh +7
We study a game between liquidity provider and liquidity taker agents interacting in an over-the-counter market, for which the typical example is foreign exchange. We show how a su…
Learning to simulate realistic limit order book markets from data as a World Agent
Andrea Coletta, Aymeric Moulin, Svitlana Vyetrenko +1
Multi-agent market simulators usually require careful calibration to emulate real markets, which includes the number and the type of agents. Poorly calibrated simulators can lead t…
Equitable Marketplace Mechanism Design
Kshama Dwarakanath, Svitlana S Vyetrenko, Tucker Balch
We consider a trading marketplace that is populated by traders with diverse trading strategies and objectives. The marketplace allows the suppliers to list their goods and facilita…
Online Learning for Mixture of Multivariate Hawkes Processes
Mohsen Ghassemi, Niccolò Dalmasso, Simran Lamba +4
Online learning of Hawkes processes has received increasing attention in the last couple of years especially for modeling a network of actors. However, these works typically either…
Differentially Private Learning of Hawkes Processes
Mohsen Ghassemi, Eleonora Kreačić, Niccolò Dalmasso +3
Hawkes processes have recently gained increasing attention from the machine learning community for their versatility in modeling event sequence data. While they have a rich history…
Collusion Resistant Federated Learning with Oblivious Distributed Differential Privacy
David Byrd, Vaikkunth Mugunthan, Antigoni Polychroniadou +1
Privacy-preserving federated learning enables a population of distributed clients to jointly learn a shared model while keeping client training data private, even from an untrusted…
CTMSTOU driven markets: simulated environment for regime-awareness in trading policies
Selim Amrouni, Aymeric Moulin, Tucker Balch
Market regimes is a popular topic in quantitative finance even though there is little consensus on the details of how they should be defined. They arise as a feature both in financ…