58 citations · 146 across the 28 of their papers we have counts for
34 papers
A closed-measure approach to stochastic approximation
Pascal Bianchi, Rodolfo Rios-Zertuche
This paper introduces a new method to tackle the issue of the almost sure convergence of stochastic approximation algorithms defined from a differential inclusion. Under the assump…
Stochastic Subgradient Descent Escapes Active Strict Saddles on Weakly Convex Functions
Pascal Bianchi, Walid Hachem, Sholom Schechtman
In non-smooth stochastic optimization, we establish the non-convergence of the stochastic subgradient descent (SGD) to the critical points recently called active strict saddles by…
Analysis of a Target-Based Actor-Critic Algorithm with Linear Function Approximation
Anas Barakat, Pascal Bianchi, Julien Lehmann
Actor-critic methods integrating target networks have exhibited a stupendous empirical success in deep reinforcement learning. However, a theoretical understanding of the use of ta…
Conditional independence testing via weighted partial copulas and nearest neighbors
Pascal Bianchi, Kevin Elgui, François Portier
This paper introduces the \textit{weighted partial copula} function for testing conditional independence. The proposed test procedure results from these two ingredients: (i) the te…
Convergence of constant step stochastic gradient descent for non-smooth non-convex functions
Pascal Bianchi, Walid Hachem, Sholom Schechtman
This paper studies the asymptotic behavior of the constant step Stochastic Gradient Descent for the minimization of an unknown function F , defined as the expectation of a non conv…
Convergence Analysis of a Momentum Algorithm with Adaptive Step Size for Non Convex Optimization
Anas Barakat, Pascal Bianchi
Although ADAM is a very popular algorithm for optimizing the weights of neural networks, it has been recently shown that it can diverge even in simple convex optimization examples.…