Olivier Wintenberger | DeepAI

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∙ 03/30/2023

Self-normalized partial sums of heavy-tailed time series

We study the joint limit behavior of sums, maxima and ℓ^p-type moduli fo...

0 Muneya Matsui, et al. ∙

∙ 01/24/2023

Adaptive Probabilistic Forecasting of Electricity (Net-)Load

We focus on electricity load forecasting under three important specifici...

0 Joseph De Vilmarest, et al. ∙

∙ 01/18/2023

Optimistic Dynamic Regret Bounds

Online Learning (OL) algorithms have originally been developed to guaran...

20 Maxime Haddouche, et al. ∙

∙ 12/27/2022

On the asymptotics of extremal lp-blocks cluster inference

Extremes occur in stationary regularly varying time series as short peri...

0 Gloria Buriticá, et al. ∙

∙ 05/25/2022

Learning from time-dependent streaming data with online stochastic algorithms

We study stochastic algorithms in a streaming framework, trained on samp...

0 Antoine Godichon-Baggioni, et al. ∙

∙ 02/22/2022

Extremes for stationary regularly varying random fields over arbitrary index sets

We consider the clustering of extremes for stationary regularly varying ...

0 Riccardo Passeggeri, et al. ∙

∙ 09/15/2021

Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Streaming Data

Motivated by the high-frequency data streams continuously generated, rea...

0 Antoine Godichon-Baggioni, et al. ∙

∙ 06/09/2021

Some variations on the extremal index

We re-consider Leadbetter's extremal index for stationary sequences. It ...

0 Gloria Buriticá, et al. ∙

∙ 02/01/2021

Stochastic Online Convex Optimization; Application to probabilistic time series forecasting

Stochastic regret bounds for online algorithms are usually derived from ...

0 Olivier Wintenberger, et al. ∙

∙ 10/08/2020

Estmiation of the Spectral Measure from Convex Combinations of Regularly Varying Random Vectors

The extremal dependence structure of a regularly varying random vector X...

0 Marco Oesting, et al. ∙

∙ 07/23/2020

Tail inference for high-dimensional data

Identifying directions where severe events occur is a major challenge in...

0 Nicolas Meyer, et al. ∙

∙ 06/03/2020

AdaVol: An Adaptive Recursive Volatility Prediction Method

Quasi-Maximum Likelihood (QML) procedures are theoretically appealing an...

0 Nicklas Werge, et al. ∙

∙ 02/26/2020

Kalman Recursions Aggregated Online

In this article, we aim at improving the prediction of expert aggregatio...

1 Eric Adjakossa, et al. ∙

∙ 02/10/2020

Stochastic Online Optimization using Kalman Recursion

We study the Extended Kalman Filter in constant dynamics, offering a bay...

0 Joseph De Vilmarest, et al. ∙

∙ 07/04/2019

Consistent Regression using Data-Dependent Coverings

In this paper, we introduce a novel method to generate interpretable reg...

0 Vincent Margot, et al. ∙

∙ 07/01/2019

Sparse regular variation

Regular variation provides a convenient theoretical framework to study l...

0 Nicolas Meyer, et al. ∙

∙ 02/26/2019

Logarithmic Regret for parameter-free Online Logistic Regression

We consider online optimization procedures in the context of logistic re...

0 Joseph De Vilmarest, et al. ∙

∙ 07/12/2018

Rule Induction Partitioning Estimator

RIPE is a novel deterministic and easily understandable prediction algor...

0 Vincent Margot, et al. ∙

∙ 05/23/2018

Efficient online algorithms for fast-rate regret bounds under sparsity

We consider the online convex optimization problem. In the setting of ar...

0 Pierre Gaillard, et al. ∙

∙ 08/19/2016

A Strongly Quasiconvex PAC-Bayesian Bound

We propose a new PAC-Bayesian bound and a way of constructing a hypothes...

0 Niklas Thiemann, et al. ∙

∙ 04/04/2014

Optimal learning with Bernstein Online Aggregation

We introduce a new recursive aggregation procedure called Bernstein Onli...

0 Olivier Wintenberger, et al. ∙

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