35 citations · 163 across the 31 of their papers we have counts for
6 papers · 1 filter
AAMDRL: Augmented Asset Management with Deep Reinforcement Learning
Eric Benhamou, David Saltiel, Sandrine Ungari +2
Can an agent learn efficiently in a noisy and self adapting environment with sequential, non-stationary and non-homogeneous observations? Through trading bots, we illustrate how De…
Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning
Eric Benhamou, David Saltiel, Jean-Jacques Ohana +1
Deep reinforcement learning (DRL) has reached super human levels in complex tasks like game solving (Go and autonomous driving). However, it remains an open question whether DRL ca…
On Lipschitz Regularization of Convolutional Layers using Toeplitz Matrix Theory
Alexandre Araujo, Benjamin Negrevergne, Yann Chevaleyre +1
This paper tackles the problem of Lipschitz regularization of Convolutional Neural Networks. Lipschitz regularity is now established as a key property of modern deep learning with…
Equitable and Optimal Transport with Multiple Agents
Meyer Scetbon, Laurent Meunier, Jamal Atif +1
We introduce an extension of the Optimal Transport problem when multiple costs are involved. Considering each cost as an agent, we aim to share equally between agents the work of t…
Estimating Individual Treatment Effects through Causal Populations Identification
Céline Beji, Michaël Bon, Florian Yger +1
Estimating the Individual Treatment Effect from observational data, defined as the difference between outcomes with and without treatment or intervention, while observing just one…
Randomization matters. How to defend against strong adversarial attacks
Rafael Pinot, Raphael Ettedgui, Geovani Rizk +2
Is there a classifier that ensures optimal robustness against all adversarial attacks? This paper answers this question by adopting a game-theoretic point of view. We show that adv…