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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...
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01/24/2023
Adaptive Probabilistic Forecasting of Electricity (Net-)Load
We focus on electricity load forecasting under three important specifici...
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01/18/2023
Optimistic Dynamic Regret Bounds
Online Learning (OL) algorithms have originally been developed to guaran...
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12/27/2022
On the asymptotics of extremal lp-blocks cluster inference
Extremes occur in stationary regularly varying time series as short peri...
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05/25/2022
Learning from time-dependent streaming data with online stochastic algorithms
We study stochastic algorithms in a streaming framework, trained on samp...
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02/22/2022
Extremes for stationary regularly varying random fields over arbitrary index sets
We consider the clustering of extremes for stationary regularly varying ...
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09/15/2021
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Streaming Data
Motivated by the high-frequency data streams continuously generated, rea...
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06/09/2021
Some variations on the extremal index
We re-consider Leadbetter's extremal index for stationary sequences. It ...
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02/01/2021
Stochastic Online Convex Optimization; Application to probabilistic time series forecasting
Stochastic regret bounds for online algorithms are usually derived from ...
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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...
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07/23/2020
Tail inference for high-dimensional data
Identifying directions where severe events occur is a major challenge in...
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06/03/2020
AdaVol: An Adaptive Recursive Volatility Prediction Method
Quasi-Maximum Likelihood (QML) procedures are theoretically appealing an...
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02/26/2020
Kalman Recursions Aggregated Online
In this article, we aim at improving the prediction of expert aggregatio...
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02/10/2020
Stochastic Online Optimization using Kalman Recursion
We study the Extended Kalman Filter in constant dynamics, offering a bay...
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07/04/2019
Consistent Regression using Data-Dependent Coverings
In this paper, we introduce a novel method to generate interpretable reg...
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07/01/2019
Sparse regular variation
Regular variation provides a convenient theoretical framework to study l...
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02/26/2019
Logarithmic Regret for parameter-free Online Logistic Regression
We consider online optimization procedures in the context of logistic re...
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07/12/2018
Rule Induction Partitioning Estimator
RIPE is a novel deterministic and easily understandable prediction algor...
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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...
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08/19/2016
A Strongly Quasiconvex PAC-Bayesian Bound
We propose a new PAC-Bayesian bound and a way of constructing a hypothes...
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04/04/2014
