Patrimony

The Louis Bachelier Group's patrimony has been defined as all the publications produced by academic researchers thanks to Group funding (ILB, FdR, IEF, Labex) or via the use of EquipEx data (BEDOFIH, EUROFIDAI).


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Non-parametric Stochastic Approximation with Large Step sizes.

Reproducing kernel Hilbert space, Stochastic approximation

Stochastic approximation in Hilbert spaces.

Apprentissage supervisé, Approximation stochastique, Convex optimization, Espaces de Hilbert à noyaux reproduisants, Estimation non-paramétrique, Nonparametric estimation, Optimisation convexe, Reproducing kernel Hilbert spaces, Stochastic approximation, Supervised learning

Stochastic approximation and least-squares regression, with applications to machine learning.

Acceleration, Accélération, Algorithme dual moyenné, Apprentissage de permutation, Approximation stochastique, Averaging, Contraintes de forme, Convex optimization, Convex relaxation, Descente miroire, Discriminative clustering, Dual averaging, Estimation minimax, Gradient stochastique, Least-squares regression, Minimax estimation, Mirror descent, Moyennage, Optimisation convexe, Parcimonie, Partionnement discriminatif, Permutation learning, Relaxation convexe, Régression par moindres carrés, Shape constraints, Sparsity, Statistical seriation, Stochastic approximation, Stochastic gradient, Sériation statistique

Optimal posting price of limit orders: learning by trading.

Co-monotony principle, Compound Poisson process, High-frequency optimal liquidation, Limit order, Market impact, Order book, Statistical learning, Stochastic approximation

Randomized Urn Models revisited using Stochastic Approximation.

Asymptotic normality, Extended Pòlya urn models, Multi-arm clinical trials, Nonhomogeneous generating matrix, Stochastic approximation, Strong consistency

Nonlinear Randomized Urn Models: a Stochastic Approximation Viewpoint.

2010 AMS classification 62L20, 62E20, 62L05 secondary 62F12, 62P10, Asymptotic normality, Bandit algorithms, Extended Pólya urn models, Generating matrix, Non-homogeneous, Non-homogeneous generating matrix, Reinforcement, Stochastic approximation, Strong consistency

Asymptotic study of stochastic algorithms and computation of Parisian options prices.

Algorithmes tronqués, Approximation stochastique, Central limit theorem, Inversion numérique, Laplace transforms, Numerical inversion, Options parisiennes, Parisian options, Stochastic approximation, Théorème centrale limite, Transformées de Laplace, Truncated algorithms

Asymptotic normality of randomly truncated stochastic algorithms.

Central limit theorem, Martingale arrays, Randomly truncated stochastic algorithms, Stochastic approximation

Asymptotic study of stochastic algorithms and computation of Parisian options prices.

Algorithmes tronqués, Approximation stochastique, Central limit theorem, Inversion numérique, Laplace transforms, Numerical inversion, Options parisiennes, Parisian options, Stochastic approximation, Théorème central limite, Transformés de Laplace, Troncated algorithms

Recursive computation of the invariant distribution of Markov and Feller processes.

Ergodic theory, Invariant measures, Limit theorem, Markov processes, Stochastic approximation

CVa R HEDGING USING QUANTIZATION-BASED STOCHASTIC APPROXIMATION ALGORITHM.

CVaR, Quantification, Robbins-Monro algorithm, Stochastic Approximation, VaR

Stochastic approximation in Hilbert spaces.

Apprentissage supervisé, Approximation stochastique, Convex optimization, Espaces de Hilbert à noyaux reproduisants, Estimation non-paramétrique, Nonparametric estimation, Optimisation convexe, Reproducing kernel Hilbert spaces, Stochastic approximation, Supervised learning