120 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- CY Cergy Paris UniversitéFR30 papers
- ESSEC Business SchoolSG11 papers
- Centre de Recherche en Économie et StatistiqueFR10 papers
- Johannes Kepler University of LinzAT4 papers
- Sorbonne UniversitéFR4 papers
- Université Paris CitéFR4 papers
- Centre for Research in Engineering Surface TechnologyIE3 papers
- Mathématiques Appliquées à Paris 5FR3 papers
- Center for Responsible TravelUS2 papers
- Decision Sciences (United States)US2 papers
- Département mathématiques, informatique, sciences de la donnée et technologies du numériqueFR2 papers
- ENSAE ParisFR2 papers
59 papers
Do designated market makers provide liquidity during downward extreme price movements?
Mario Bellia, Kim Christensen, Aleksey Kolokolov +2
We study the trading activity of designated market makers (DMMs) in electronic markets using a unique dataset with audit-trail information on trader classification. DMMs may either…
Statistical Inference in Large Multi-way Networks
Lucas Resende, Guillaume Lecué, Lionel Wilner +1
We propose the Polyads estimator, a new method to estimate structural parameters in weighted multi-way networks while controlling for rich, arbitrary structures of fixed effects. T…
Multivariate Discrete Generalized Pareto Distributions: Theory, Simulation, and Applications to Dry spells
Samira Aka, Marie Kratz, Philippe Naveau
This article extends the multivariate extreme value theory (MEVT) to discrete settings, focusing on the generalized Pareto distribution (GPD) as a foundational tool. The purpose of…
Low-Rank Graphon Estimation: Theory and Applications to Graphon Games
Olga Klopp, Fedor Noskov
We study low-rank estimation of an unknown sparse graphon from sampled network data under operator-norm loss, motivated by targeted interventions in graphon games. Starting from th…
On importance sampling and independent Metropolis-Hastings with an unbounded weight function
George Deligiannidis, Pierre E. Jacob, El Mahdi Khribch +1
Importance sampling and independent Metropolis-Hastings are among the fundamental building blocks of Monte Carlo methods. Both require a proposal distribution that globally approxi…
Minimax optimality of deep neural networks on dependent data via PAC-Bayes bounds
Pierre Alquier, William Kengne
In a groundbreaking work, Schmidt-Hieber (2020) proved the minimax optimality of deep neural networks with ReLu activation for least-square regression estimation over a large class…
Learning with Importance Weighted Variational Inference
Kamélia Daudel, François Roueff
Several variational bounds involving importance weighting ideas generalize the Evidence Lower BOund (ELBO) for marginal likelihood optimization, such as the Importance-weighted Aut…
The Multi-Commodity Flow Problem with Outsourcing Decisions
Elena Fernandez, Ivana Ljubic, Nicolas Zerega
We address a new prize-collecting problem of routing commodities in a given network with hub and non-hub nodes, in which the service of the non-hub nodes will be outsourced to thir…
Bilevel optimization with sustainability perspective: A survey on applications
Giulia Caselli, Manuel Iori, Ivana Ljubić
Bilevel optimization, a well-established field for modeling hierarchical decision-making problems, has recently intersected with sustainability studies and practices, resulting in…
Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning
Otmane Sakhi, Imad Aouali, Pierre Alquier +1
This work investigates the offline formulation of the contextual bandit problem, where the goal is to leverage past interactions collected under a behavior policy to evaluate, sele…
Aircraft Conflict Resolution: A Benchmark Generator
Mercedes Pelegrin, Martina Cerulli
Aircraft conflict resolution is one of the major tasks of computer-aided air traffic management and represents a challenging optimization problem. Many models and methods have been…
One-Bit Total Variation Denoising over Networks with Applications to Partially Observed Epidemics
Claire Donnat, Olga Klopp, Nicolas Verzelen
This paper introduces a novel approach for epidemic nowcasting and forecasting over networks using total variation (TV) denoising, a method inspired by classical signal processing…
Generalized multi-view model: Adaptive density estimation under low-rank constraints
Julien Chhor, Olga Klopp, Alexandre Tsybakov
We study the problem of bivariate discrete or continuous probability density estimation under low-rank constraints.For discrete distributions, we assume that the two-dimensional ar…
Alleviating the quantum Big- problem
Edoardo Alessandroni, Sergi Ramos-Calderer, Ingo Roth +2
A major obstacle for quantum optimizers is the reformulation of constraints as a quadratic unconstrained binary optimization (QUBO). Current QUBO translators exaggerate the weight…
What Does it Take to Control Global Temperatures? A toolbox for testing and estimating the impact of economic policies on climate
Guillaume Chevillon, Takamitsu Kurita
This paper tests the feasibility and estimates the cost of climate control through economic policies. It provides a toolbox for a statistical historical assessment of a Stochastic…
A Decomposition Approach to Last Mile Delivery Using Public Transportation Systems
Minakshi Punam Mandal, Claudia Archetti
This study explores the potential of using public transportation systems for freight delivery, where we intend to utilize the spare capacities of public vehicles like buses, trams,…
Extremal behaviour and convergence rates for sample--based geometric quantiles and half space depths
Sibsankar Singha, Marie Kratz, Sreekar Vadlamani
We consider the empirical versions of geometric quantile and halfspace depth, and study their extremal behaviour as a function of the sample size. The objective of this study is to…
Change point detection in low-rank VAR processes
Farida Enikeeva, Olga Klopp, Mathilde Rousselot
Vector autoregressive (VAR) models are widely used in multivariate time series analysis for describing the short-time dynamics of the data. The reduced-rank VAR models are of parti…
Efficient Estimation in Extreme Value Regression Models of Hedge Fund Tail Risks
Julien Hambuckers, Marie Kratz, Antoine Usseglio-Carleve
We introduce a method to estimate simultaneously the tail and the threshold parameters of an extreme value regression model. This standard model finds its use in finance to assess…
A bilevel approach for compensation and routing decisions in last-mile delivery
Martina Cerulli, Claudia Archetti, Elena Fernandez +1
In last-mile delivery logistics, peer-to-peer logistic platforms play an important role in connecting senders, customers, and independent carriers to fulfill delivery requests. Sin…
Operational Research: Methods and Applications
Fotios Petropoulos, Gilbert Laporte, Emel Aktas +79
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This…
Bayes meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes
Charles Riou, Pierre Alquier, Badr-Eddine Chérief-Abdellatif
Bernstein's condition is a key assumption that guarantees fast rates in machine learning. For example, the Gibbs algorithm with prior has an excess risk in , as oppos…
Managing Cyber Risk, a Science in the Making
Michel Dacorogna, Marie Kratz
Not a day goes by without news about a cyber attack. Fear spreads out and lots of wrong ideas circulate. This survey aims at showing how all these uncertainties about cyber can be…
On the relation between extremal dependence and concomitants
Amir Khorrami Chokami, Marie Kratz
The study of concomitants has recently met a renewed interest due to its applications in selection procedures. For instance, concomitants are used in ranked-set sampling, to achiev…