5 papers
Optimizing Optimizers: Regret-optimal gradient descent algorithms
Philippe Casgrain, Anastasis Kratsios
The need for fast and robust optimization algorithms are of critical importance in all areas of machine learning. This paper treats the task of designing optimization algorithms as…
A Latent Variational Framework for Stochastic Optimization
Philippe Casgrain
This paper provides a unifying theoretical framework for stochastic optimization algorithms by means of a latent stochastic variational problem. Using techniques from stochastic co…
Mean-Field Games with Differing Beliefs for Algorithmic Trading
Philippe Casgrain, Sebastian Jaimungal
Even when confronted with the same data, agents often disagree on a model of the real-world. Here, we address the question of how interacting heterogenous agents, who disagree on w…
Trading algorithms with learning in latent alpha models
Philippe Casgrain, Sebastian Jaimungal
Alpha signals for statistical arbitrage strategies are often driven by latent factors. This paper analyses how to optimally trade with latent factors that cause prices to jump and…
Mean Field Games with Partial Information for Algorithmic Trading
Philippe Casgrain, Sebastian Jaimungal
Financial markets are often driven by latent factors which traders cannot observe. Here, we address an algorithmic trading problem with collections of heterogeneous agents who aim…