Search Results for author:
Found 41 papers, 13 papers with code
Collaborative non-parametric two-sample testing
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.
Stein Boltzmann Sampling: A Variational Approach for Global Optimization
In this paper, we introduce a new flow-based method for global optimization of Lipschitz functions, called Stein Boltzmann Sampling (SBS).
Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization
Quantifying the difference between two probability density functions, $p$ and $q$, using available data, is a fundamental problem in Statistics and Machine Learning.
A framework for paired-sample hypothesis testing for high-dimensional data
First, we estimate the bisecting hyperplanes for each pair of instances and an aggregated rule derived through the Hodges-Lehmann estimator.
Maximum Weight Entropy
Under this paradigm, the epistemic uncertainty is described by the weight distribution of maximal entropy that produces neural networks "consistent" with the training observations.
Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification
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.
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.
Fast and Accurate Importance Weighting for Correcting Sample Bias
Bias in datasets can be very detrimental for appropriate statistical estimation.
Collaborative likelihood-ratio estimation over graphs
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.
Online non-parametric change-point detection for heterogeneous data streams observed over graph nodes
Consider a heterogeneous data stream being generated by the nodes of a graph.
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.
Concentration Inequalities for Two-Sample Rank Processes with Application to Bipartite Ranking
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.
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.
Robust Kernel Density Estimation with Median-of-Means principle
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).
Offline detection of change-points in the mean for stationary graph signals
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.
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.
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.
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.
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 process (DP) (e. g. an epidemic).
Multivariate Convolutional Sparse Coding with Low Rank Tensor
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.
Revealing posturographic features associated with the risk of falling in patients with Parkinsonian syndromes via machine learning
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).
DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding
In this paper, we introduce DICOD, a convolutional sparse coding algorithm which builds shift invariant representations for long signals.
ruptures: change point detection in Python
ruptures is a Python library for offline change point detection.
Computation Mathematical Software
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.
Computational Engineering, Finance, and Science Methodology
A Spectral Method for Activity Shaping in Continuous-Time Information Cascades
Information Cascades Model captures dynamical properties of user activity in a social network.
DICOD: Distributed Convolutional Sparse Coding
In this paper, we introduce DICOD, a convolutional sparse coding algorithm which builds shift invariant representations for long signals.
Global optimization of Lipschitz functions
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.
A ranking approach to global optimization
We consider the problem of maximizing an unknown function over a compact and convex set using as few observations as possible.
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.
Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks
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.
Optimization for Gaussian Processes via Chaining
In this paper, we consider the problem of stochastic optimization under a bandit feedback model.
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).
Gaussian Process Optimization with Mutual Information
In this paper, we analyze a generic algorithm scheme for sequential global optimization using Gaussian processes.
Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration
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.
Link Prediction in Graphs with Autoregressive Features
In the paper, we consider the problem of link prediction in time-evolving graphs.
Estimation of Simultaneously Sparse and Low Rank Matrices
The paper introduces a penalized matrix estimation procedure aiming at solutions which are sparse and low-rank at the same time.
Link Discovery using Graph Feature Tracking
We consider the problem of discovering links of an evolving undirected graph given a series of past snapshots of that graph.
AUC optimization and the two-sample problem
A nearly optimal scoring function in the AUC sense is first learnt from one of the two half-samples.
Empirical performance maximization for linear rank statistics
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.
Overlaying classifiers: a practical approach for optimal ranking
ROC curves are one of the most widely used displays to evaluate performance of scoring functions.
On Bootstrapping the ROC Curve
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.
