11 citations · 14 across the 4 of their papers we have counts for
7 papers
Anytime-valid sequential testing for elicitable functionals via supermartingales
Philippe Casgrain, Martin Larsson, Johanna Ziegel
We design sequential tests for a large class of nonparametric null hypotheses based on elicitable and identifiable functionals. Such functionals are defined in terms of scoring fun…
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…
Deep Q-Learning for Nash Equilibria: Nash-DQN
Philippe Casgrain, Brian Ning, Sebastian Jaimungal
Model-free learning for multi-agent stochastic games is an active area of research. Existing reinforcement learning algorithms, however, are often restricted to zero-sum games, and…
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…