Nicolas Vayatis
87 papers 1.9K citations h-index 10 1999–2026
ADAPT : Awesome Domain Adaptation Python Toolbox
In this paper, we introduce the ADAPT library, an open source Python API providing the implementation of the main transfer learning and domain adaptation methods. The library is designed with a...
Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification
We consider the problem of uncertainty quantification in high dimensional regression and classification for which deep ensemble have proven to be promising methods. Recent observations have shown...
Selective review of offline change point detection methods
This article presents a selective survey of algorithms for the offline detection of multiple change points in multivariate time series. A general yet structuring methodological strategy is adopted...
Fast and Accurate Importance Weighting for Correcting Sample Bias
Bias in datasets can be very detrimental for appropriate statistical estimation. In response to this problem, importance weighting methods have been developed to match any biased distribution to...
Discrepancy-Based Active Learning for Domain Adaptation
The goal of the paper is to design active learning strategies which lead to domain adaptation under an assumption of Lipschitz functions. Building on previous work by Mansour et al. (2009) we...
Adversarial Weighting for Domain Adaptation in Regression
We present a novel instance-based approach to handle regression tasks in the context of supervised domain adaptation under an assumption of covariate shift. The approach developed in this paper is...
Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation
Consider each node of a graph to be generating a data stream that is synchronized and observed at near real-time. At a change-point $\tau$, a change occurs at a subset of nodes $C$, which affects...
ruptures: change point detection in Python
ruptures is a Python library for offline change point detection. This package provides methods for the analysis and segmentation of non-stationary signals. Implemented algorithms include exact and...
Sequential Dynamic Resource Allocation for Epidemic Control
Under the Dynamic Resource Allocation (DRA) model, an administrator has the mission to allocate dynamically a limited budget of resources to the nodes of a network in order to reduce a diffusion...
Optimal Multiple Stopping Rule for Warm-Starting Sequential Selection
In this paper we present the Warm-starting Dynamic Thresholding algorithm, developed using dynamic programming, for a variant of the standard online selection problem. The problem allows job...
The Warm-starting Sequential Selection Problem and its Multi-round Extension
In the Sequential Selection Problem (SSP), immediate and irrevocable decisions need to be made as candidates randomly arrive for a job interview. Standard SSP variants, such as the well-known...
Learning the piece-wise constant graph structure of a varying Ising model
This work focuses on the estimation of multiple change-points in a time-varying Ising model that evolves piece-wise constantly. The aim is to identify both the moments at which significant changes...
Revealing posturographic features associated with the risk of falling in patients with Parkinsonian syndromes via machine learning
Falling in Parkinsonian syndromes (PS) is associated with postural instability and consists a common cause of disability among PS patients. Current posturographic practices record the body's...
A Spectral Method for Activity Shaping in Continuous-Time Information Cascades
Information Cascades Model captures dynamical properties of user activity in a social network. In this work, we develop a novel framework for activity shaping under the Continuous-Time Information...
Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration
In this paper, we consider the challenge of maximizing an unknown function f for which evaluations are noisy and are acquired with high cost. An iterative procedure uses the previous measures to...
Stochastic Process Bandits: Upper Confidence Bounds Algorithms via Generic Chaining
The paper considers the problem of global optimization in the setup of stochastic process bandits. We introduce an UCB algorithm which builds a cascade of discretization trees based on generic...
Gaussian Process Optimization with Mutual Information
In this paper, we analyze a generic algorithm scheme for sequential global optimization using Gaussian processes. The upper bounds we derive on the cumulative regret for this generic algorithm...
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology
In this paper, we derive theoretical bounds for the long-term influence of a node in an Independent Cascade Model (ICM). We relate these bounds to the spectral radius of a particular matrix and...