Publications related to Feature selection

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification

Informative sample selection in an active learning (AL) setting helps a machine learning system attain optimum performance with minimum labeled samples, thus reducing annotation costs and boosting performance of computer-aided diagnosis systems in the pres ...

Elsevier2024

Euclid preparation XXXVII. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses

Frédéric Courbin, Georges Meylan, Gianluca Castignani, Maurizio Martinelli, Matthias Wiesmann, Yi Wang, Richard Massey, Fabio Finelli, Marcello Farina

Aims. We derived galaxy colour selections from Euclid and ground-based photometry, aiming to accurately define background galaxy samples in cluster weak-lensing analyses. These selections have been implemented in the Euclid data analysis pipelines for gala ...

Edp Sciences S A2024

Biases in Information Selection and Processing: Survey Evidence from the Pandemic

Andreas Fuster

We conduct two survey experiments to study which information people choose to consume and how it affects their beliefs. In the first experiment, respondents choose between optimistic and pessimistic article headlines related to the COVID-19 pandemic and ar ...

2024

Efficient local linearity regularization to overcome catastrophic overfitting

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Elias Abad Rocamora

Catastrophic overfitting (CO) in single-step adversarial training (AT) results in abrupt drops in the adversarial test accuracy (even down to 0%). For models trained with multi-step AT, it has been observed that the loss function behaves locally linearly w ...

2024

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Vinitra Swamy, Paola Mejia Domenzain, Tanja Christina Käser Jacober, Julian Thomas Blackwell, Isadora Alves de Salles

Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

2024

Robust Outlier Rejection for 3D Registration with Variational Bayes

Mathieu Salzmann, Zheng Dang, Jiancheng Yang, Zhen Wei, Haobo Jiang

Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier f ...

Ieee Computer Soc2023

Anomalous Dissipation and Lack of Selection in the Obukhov-Corrsin Theory of Scalar Turbulence

Maria Colombo, Massimo Sorella

The Obukhov-Corrsin theory of scalar turbulence [21, 54] advances quantitative predictions on passive-scalar advection in a turbulent regime and can be regarded as the analogue for passive scalars of Kolmogorov's K41 theory of fully developed turbulence [4 ...

Springernature2023

DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices

Olga Fink, Ismail Nejjar

Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain gap between a labeled source dataset and an unlabelled target dataset for regression problems. Recent works mostly focus on learning a deep feature encoder by minimizing the discrepa ...

IEEE2023

Improving Generalization of Pretrained Language Models

Rabeeh Karimi Mahabadi

In this dissertation, we propose multiple methods to improve transfer learning for pretrained language models (PLMs). Broadly, transfer learning is a powerful technique in natural language processing, where a language model is first pre-trained on a data-r ...

EPFL2023

Self-Interaction and Polarons in Density Functional Theory

Stefano Falletta

The electron self-interaction is a long-standing problem in density functional theory and is particularly critical in the description of polarons. Polarons are quasiparticles involving charge localization coupled with self-induced lattice distortions. Sinc ...

EPFL2023

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Florent Gérard Krzakala, Lenka Zdeborová, Hugo Chao Cui

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

2023

Data-driven approaches for non-invasive cuffless blood pressure monitoring

Clémentine Léa Aguet

Blood pressure (BP) is a crucial indicator of cardiovascular health. Hypertension is a common life-threatening condition and a key factor of cardiovascular diseases (CVDs). Identifying abnormal BP fluctuations can allow for early detection and management o ...

EPFL2023

Optimal digital filter selection for remote photoplethysmography (rPPG) signal conditioning

Ata Jedari Golparvar

Remote photoplethysmography (rPPG) using camera-based imaging has shown excellent potential recently in vital signs monitoring due to its contactless nature. However, the optimum filter selection for pre-processing rPPG data in signal conditioning is still ...

IOP Publishing Ltd2023

Hardware Implementation of Digital Signal Processing Algorithms for Programmable Epilepsy Control Systems

Keyvan Farhang Razi

Epilepsy is a common chronic neurological disorder that causes recurring seizures and affects more than 50 million people worldwide. Implantable medical devices (IMDs) are regarded as effective tools to cure patients who suffer from refractory epilepsy. Se ...

EPFL2023

Data-driven optimization tool for the functional, economic, and environmental properties of blended cement concrete using supplementary cementitious materials

Hisham Tarek Mohamed Hafez

The need to produce more sustainable concrete is proving imminent given the rising environmental concerns facing the industry. Blended cement concrete, based on any of the prominent supplementary cementitious materials (SCMs) such as fly ash, ground granul ...

ELSEVIER2023

A Comparison of Residual-based Methods on Fault Detection

Olga Fink, Gaëtan Michel Frusque

An important initial step in fault detection for complex industrial systems is gaining an understanding of their health condition. Subsequently, continuous monitoring of this health condition becomes crucial to observe its evolution, track changes over tim ...

PHM Society2023

Distribution Inference Risks: Identifying and Mitigating Sources of Leakage

Robert West, Maxime Jean Julien Peyrard, Valentin Hartmann, Léo Nicolas René Meynent

A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inference (or property inference) attacks is gaining attention. In this attack, the ...

IEEE COMPUTER SOC2023

Systematic benchmarking of single-cell ATAC-sequencing protocols

Bart Deplancke, Jörn Pezoldt, Camille Lucie Germaine Lambert

Benchmarking of scATAC-seq protocols demonstrates the effects of data quality on downstream analyses. ...

NATURE PORTFOLIO2023

Auto-tuning ensemble models for estimating shear resistance of headed studs in concrete

Xianlin Wang, Yuqing Liu

The shear resistance of headed studs is of paramount importance for the design of steel-concrete composite structures and an accurate predictive model is highly needed. Ensemble learning is expected to be a powerful solution while it relies on laborious se ...

2022

Metric learning for kernel ridge regression: assessment of molecular similarity

Friedrich Eisenbrand, Anne-Clémence Corminboeuf, Puck Elisabeth van Gerwen, Raimon Fabregat I De Aguilar-Amat

Supervised and unsupervised kernel-based algorithms widely used in the physical sciences depend upon the notion of similarity. Their reliance on pre-defined distance metrics-e.g. the Euclidean or Manhattan distance-are problematic especially when used in c ...

IOP Publishing Ltd2022