no code implementations • 8 Feb 2024 • Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos

This paper addresses the multiple two-sample test problem in a graph-structured setting, which is a common scenario in fields such as Spatial Statistics and Neuroscience.

1 code implementation • 7 Feb 2024 • Gaëtan Serré, Argyris Kalogeratos, Nicolas Vayatis

In this paper, we present a flow-based method for global optimization of continuous Sobolev functions, called Stein Boltzmann Sampling (SBS).

no code implementations • 3 Nov 2023 • Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos

Quantifying the difference between two probability density functions, $p$ and $q$, using available data, is a fundamental problem in Statistics and Machine Learning.

no code implementations • 28 Sep 2023 • Ioannis Bargiotas, Argyris Kalogeratos, Nicolas Vayatis

First, we estimate the bisecting hyperplanes for each pair of instances and an aggregated rule derived through the Hodges-Lehmann estimator.

2 code implementations • 27 Sep 2023 • Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis

Under this paradigm, the epistemic uncertainty is described by the weight distribution of maximal entropy that produces neural networks "consistent" with the training observations.

no code implementations • 8 Apr 2023 • Antoine de Mathelin, Francois Deheeger, Mathilde Mougeot, Nicolas Vayatis

We derive a simple and practical approach to produce such ensembles, based on an original anti-regularization term penalizing small weights and a control process of the weight increase which maintains the in-distribution loss under an acceptable threshold.

no code implementations • 8 Jan 2023 • Alejandro de la Concha, Argyris Kalogeratos, Nicolas Vayatis

Consider each node of a graph to be generating a data stream that is synchronized and observed at near real-time.

1 code implementation • 9 Sep 2022 • Antoine de Mathelin, Francois Deheeger, Mathilde Mougeot, Nicolas Vayatis

Bias in datasets can be very detrimental for appropriate statistical estimation.

no code implementations • 28 May 2022 • Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos

In this paper, we introduce the first -to the best of our knowledge-graph-based extension of this problem, which reads as follows: Suppose each node $v$ of a fixed graph has access to observations coming from two unknown node-specific pdfs, $p_v$ and $q_v$, and the goal is to estimate for each node the likelihood-ratio between both pdfs by also taking into account the information provided by the graph structure.

no code implementations • 20 Oct 2021 • Alejandro de la Concha, Argyris Kalogeratos, Nicolas Vayatis

Consider a heterogeneous data stream being generated by the nodes of a graph.

1 code implementation • 7 Jul 2021 • Antoine de Mathelin, Mounir Atiq, Guillaume Richard, Alejandro de la Concha, Mouad Yachouti, François Deheeger, Mathilde Mougeot, Nicolas Vayatis

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.

1 code implementation • 7 Apr 2021 • Stéphan Clémençon, Myrto Limnios, Nicolas Vayatis

The ROC curve is the gold standard for measuring the performance of a test/scoring statistic regarding its capacity to discriminate between two statistical populations in a wide variety of applications, ranging from anomaly detection in signal processing to information retrieval, through medical diagnosis.

2 code implementations • ICLR 2022 • Antoine de Mathelin, Francois Deheeger, Mathilde Mougeot, Nicolas Vayatis

The goal of the paper is to design active learning strategies which lead to domain adaptation under an assumption of Lipschitz functions.

1 code implementation • 30 Jun 2020 • Pierre Humbert, Batiste Le Bars, Ludovic Minvielle, Nicolas Vayatis

In this paper, we introduce a robust nonparametric density estimator combining the popular Kernel Density Estimation method and the Median-of-Means principle (MoM-KDE).

1 code implementation • 18 Jun 2020 • Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos

This paper addresses the problem of segmenting a stream of graph signals: we aim to detect changes in the mean of a multivariate signal defined over the nodes of a known graph.

2 code implementations • 15 Jun 2020 • Antoine de Mathelin, Guillaume Richard, Francois Deheeger, Mathilde Mougeot, Nicolas Vayatis

We present a novel instance-based approach to handle regression tasks in the context of supervised domain adaptation under an assumption of covariate shift.

no code implementations • 12 Feb 2020 • Mathilde Fekom, Nicolas Vayatis, Argyris Kalogeratos

In this paper we present the Warm-starting Dynamic Thresholding algorithm, developed using dynamic programming, for a variant of the standard online selection problem.

no code implementations • ICML 2020 • Batiste Le Bars, Pierre Humbert, Argyris Kalogeratos, Nicolas Vayatis

This work focuses on the estimation of multiple change-points in a time-varying Ising model that evolves piece-wise constantly.

no code implementations • 20 Sep 2019 • Mathilde Fekom, Nicolas Vayatis, Argyris Kalogeratos

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 process (DP) (e. g. an epidemic).

no code implementations • 9 Aug 2019 • Pierre Humbert, Julien Audiffren, Laurent Oudre, Nicolas Vayatis

This paper introduces a new multivariate convolutional sparse coding based on tensor algebra with a general model enforcing both element-wise sparsity and low-rankness of the activations tensors.

no code implementations • 15 Jul 2019 • Ioannis Bargiotas, Argyris Kalogeratos, Myrto Limnios, Pierre-Paul Vidal, Damien Ricard, Nicolas Vayatis

In this work, we present the ts-AUC, a non-parametric multivariate two-sample test, which we employ to analyze statokinesigram differences among PS patients that are fallers (PSf) and non-fallers (PSNF).

1 code implementation • ICML 2018 • Thomas Moreau, Laurent Oudre, Nicolas Vayatis

In this paper, we introduce DICOD, a convolutional sparse coding algorithm which builds shift invariant representations for long signals.

2 code implementations • 2 Jan 2018 • Charles Truong, Laurent Oudre, Nicolas Vayatis

This article presents a selective survey of algorithms for the offline detection of multiple change points in multivariate time series.

Computational Engineering, Finance, and Science Methodology

1 code implementation • 2 Jan 2018 • Charles Truong, Laurent Oudre, Nicolas Vayatis

ruptures is a Python library for offline change point detection.

Computation Mathematical Software

no code implementations • 15 Sep 2017 • Kevin Scaman, Argyris Kalogeratos, Luca Corinzia, Nicolas Vayatis

Information Cascades Model captures dynamical properties of user activity in a social network.

no code implementations • 29 May 2017 • Thomas Moreau, Laurent Oudre, Nicolas Vayatis

In this paper, we introduce DICOD, a convolutional sparse coding algorithm which builds shift invariant representations for long signals.

3 code implementations • ICML 2017 • Cédric Malherbe, Nicolas Vayatis

The goal of the paper is to design sequential strategies which lead to efficient optimization of an unknown function under the only assumption that it has a finite Lipschitz constant.

no code implementations • 14 Mar 2016 • Cédric Malherbe, Nicolas Vayatis

We consider the problem of maximizing an unknown function over a compact and convex set using as few observations as possible.

no code implementations • 16 Feb 2016 • Emile Contal, Nicolas Vayatis

The paper considers the problem of global optimization in the setup of stochastic process bandits.

no code implementations • NeurIPS 2015 • Kevin Scaman, Rémi Lemonnier, Nicolas Vayatis

Using this concept, we prove tight non-asymptotic bounds for the influence of a set of nodes, and we also provide an in-depth analysis of the critical time after which the contagion becomes super-critical.

no code implementations • 19 Oct 2015 • Emile Contal, Cédric Malherbe, Nicolas Vayatis

In this paper, we consider the problem of stochastic optimization under a bandit feedback model.

no code implementations • NeurIPS 2014 • Remi Lemonnier, Kevin Scaman, Nicolas Vayatis

In this paper, we derive theoretical bounds for the long-term influence of a node in an Independent Cascade Model (ICM).

no code implementations • 19 Nov 2013 • Emile Contal, Vianney Perchet, Nicolas Vayatis

In this paper, we analyze a generic algorithm scheme for sequential global optimization using Gaussian processes.

no code implementations • 19 Apr 2013 • Emile Contal, David Buffoni, Alexandre Robicquet, Nicolas Vayatis

We prove theoretical upper bounds on the regret with batches of size K for this procedure which show the improvement of the order of sqrt{K} for fixed iteration cost over purely sequential versions.

no code implementations • NeurIPS 2012 • Emile Richard, Stephane Gaiffas, Nicolas Vayatis

In the paper, we consider the problem of link prediction in time-evolving graphs.

1 code implementation • 27 Jun 2012 • Emile Richard, Pierre-Andre Savalle, Nicolas Vayatis

The paper introduces a penalized matrix estimation procedure aiming at solutions which are sparse and low-rank at the same time.

no code implementations • NeurIPS 2010 • Emile Richard, Nicolas Baskiotis, Theodoros Evgeniou, Nicolas Vayatis

We consider the problem of discovering links of an evolving undirected graph given a series of past snapshots of that graph.

no code implementations • NeurIPS 2009 • Nicolas Vayatis, Marine Depecker, Stéphan J. Clémençcon

A nearly optimal scoring function in the AUC sense is first learnt from one of the two half-samples.

no code implementations • NeurIPS 2008 • Stéphan J. Clémençcon, Nicolas Vayatis

ROC curves are one of the most widely used displays to evaluate performance of scoring functions.

no code implementations • NeurIPS 2008 • Stéphan J. Clémençcon, Nicolas Vayatis

The ROC curve is known to be the golden standard for measuring performance of a test/scoring statistic regarding its capacity of discrimination between two populations in a wide variety of applications, ranging from anomaly detection in signal processing to information retrieval, through medical diagnosis.

no code implementations • NeurIPS 2008 • Patrice Bertail, Stéphan J. Clémençcon, Nicolas Vayatis

This paper is devoted to thoroughly investigating how to bootstrap the ROC curve, a widely used visual tool for evaluating the accuracy of test/scoring statistics in the bipartite setup.

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