97 citations
- Laboratoire de Probabilités et Modèles AléatoiresFR238 papers
- Sorbonne UniversitéFR182 papers
- Centre National de la Recherche ScientifiqueFR143 papers
- Université Paris CitéFR90 papers
- Sorbonne Paris CitéFR46 papers
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- École Normale Supérieure - PSLFR28 papers
- Laboratoire de Mathématiques d'OrsayFR28 papers
- Département de mathématiques et applicationsFR25 papers
- École PolytechniqueFR24 papers
- Institut Universitaire de FranceFR23 papers
- Université Paris DiderotFR23 papers
21 papers · 1 filter
Polyak-Łojasiewicz inequality is essentially no more general than strong convexity for functions
Aziz Ben Nejma
The Polyak-Łojasiewicz (PŁ) inequality extends the favorable optimization properties of strongly convex functions to a broader class of functions. In this paper, we prove a theorem…
The non-linear multiple stopping problem: between the discrete and the continuous time
Miryana Grigorova, Marie-Claire Quenez, Peng Yuan
We consider the non-linear optimal multiple stopping problem under general conditions on the non-linear evaluation operators, which might depend on two time indices: the time of ev…
A randomisation method for mean-field control problems with common noise
Robert Denkert, Idris Kharroubi, Huyên Pham
We study mean-field control (MFC) problems with common noise using the control randomisation framework, where we substitute the control process with an independent Poisson point pr…
Full error analysis of policy gradient learning algorithms for exploratory linear quadratic mean-field control problem in continuous time with common noise
Noufel Frikha, Huyên Pham, Xuanye Song
We consider reinforcement learning (RL) methods for finding optimal policies in linear quadratic (LQ) mean field control (MFC) problems over an infinite horizon in continuous time,…
Online estimation of the inverse of the Hessian for stochastic optimization with application to universal stochastic Newton algorithms
Antoine Godichon-Baggioni, Wei Lu, Bruno Portier
This paper addresses second-order stochastic optimization for estimating the minimizer of a convex function written as an expectation. A direct recursive estimation technique for t…
Efficient Model-Based Concave Utility Reinforcement Learning through Greedy Mirror Descent
Bianca Marin Moreno, Margaux Brégère, Pierre Gaillard +1
Many machine learning tasks can be solved by minimizing a convex function of an occupancy measure over the policies that generate them. These include reinforcement learning, imitat…