Thompson Sampling: An Asymptotically Optimal Finite Time Analysis

The question of the optimality of Thompson Sampling for solving the stochastic multi-armed bandit problem had been open since 1933. In this paper we answer it positively for the case of Bernoulli rewards by providing the first finite-time analysis that matches the asymptotic rate given in the Lai and Robbins lower bound for the cumulative regret. The proof is accompanied by a numerical comparison with other optimal policies, experiments that have been lacking in the literature until now for the Bernoulli case.Comment: 15 pages, 2 figures, submitted to ALT (Algorithmic Learning Theory

Study of Interaction Modes in Pyrene-Based Fluorescent Organogels

International audienc

Precautionary Measures for Credit Risk Management in Jump Models

Sustaining efficiency and stability by properly controlling the equity to asset ratio is one of the most important and difficult challenges in bank management. Due to unexpected and abrupt decline of asset values, a bank must closely monitor its net worth as well as market conditions, and one of its important concerns is when to raise more capital so as not to violate capital adequacy requirements. In this paper, we model the tradeoff between avoiding costs of delay and premature capital raising, and solve the corresponding optimal stopping problem. In order to model defaults in a bank's loan/credit business portfolios, we represent its net worth by Levy processes, and solve explicitly for the double exponential jump diffusion process and for a general spectrally negative Levy process.Comment: 31 pages, 4 figure

Asymptotic Normality of a Class of Adaptive Statistics with Applications to Synthetic Data Methods for Censored Regression

AbstractMotivated by regression analysis of censored survival data, we develop herein a general asymptotic distribution theory for estimators defined by estimating equations of the form ∑ni=1ξ (wi, θ, Ĝn) = 0, in which wi represents observed data, θ is an unknown parameter to be estimated, and Ĝn represents an estimate of some unknown underlying distribution. This general theory is used to establish asymptotic normality of synthetic least squares estimates in censored regression models and to evaluate the covariance matrices of the limiting normal distributions

A Neural Networks Committee for the Contextual Bandit Problem

This paper presents a new contextual bandit algorithm, NeuralBandit, which does not need hypothesis on stationarity of contexts and rewards. Several neural networks are trained to modelize the value of rewards knowing the context. Two variants, based on multi-experts approach, are proposed to choose online the parameters of multi-layer perceptrons. The proposed algorithms are successfully tested on a large dataset with and without stationarity of rewards.Comment: 21st International Conference on Neural Information Processin

Revisiting urea-based gelators: strong solvent- and casting-microstructure dependencies and organogel processing using an alumina template

Urea-based gelators have been thoroughly characterized through various techniques and exhibit a strong solvent-structuration dependency in both the gel and the xerogel states. In a ground-breaking manner, gels were introduced in alumina membranes, which act as templates, in order to shape these materials and force the alignment of the corresponding self-assembled nanofibers by confinement

Internal Probing of the Supramolecular Organization of Pyrene-Based Organogelators

A thorough study of the unexpected spectroscopic behavior of two new luminescent pyrene-urea-based organogelators is rationalized as a function of their aggregation state and provides a key method to probe the supramolecular organization of the material

Study of Photoactive Organic Gels

Date du colloque : 06/2013</p

Study of Photoactive Organic Gels and their Structure

Date du colloque : 06/2012</p