Publications associées à Backdoor learning curves: explaining backdoor poisoning beyond influence functions

Publications associées (11)

From Prediction to Prescription: Machine Learning and Causal Inference for the Heterogeneous Treatment Effect

The increasing accumulation of medical data brings the hope of data-driven medical decision-making, but data's increasing complexity—as text or images in electronic health records—calls for complex models, such as machine learning. Here, we review how mach ...

Annual Reviews2025

Reducing Annotation Efforts in Electricity Theft Detection Through Optimal Sample Selection

Wenlong Liao

Supervised machine learning models are receiving increasing attention in electricity theft detection due to their high detection accuracy. However, their performance depends on a massive amount of labeled training data, which comes from time-consuming and ...

2024

Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Guillaume Marc Georges Vray, Devavrat Tomar

Developing computational pathology models is essential for reducing manual tissue typing from whole slide images, transferring knowledge from the source domain to an unlabeled, shifted target domain, and identifying unseen categories. We propose a practica ...

Institute of Electrical and Electronics Engineers2024

Prompt-guided and multimodal landscape scenicness assessments with vision-language models

Devis Tuia

Recent advances in deep learning and Vision-Language Models (VLM) have enabled efficient transfer to downstream tasks even when limited labelled training data is available, as well as for text to be directly compared to image content. These properties of V ...

PUBLIC LIBRARY SCIENCE2024

A Machine-Learning Architecture for Sensor Fault Detection, Isolation, and Accommodation in Digital Twins

Hossein Darvishi

Sensor technologies empower Industry 4.0 by enabling integration of in-field and real-time raw data into digital twins (DTs). However, sensors might be unreliable due to inherent issues and/or environmental conditions. This article aims at detecting anomal ...

2023

How Close are Predictive Models to Teachers in Detecting Learners at Risk?

Tanja Christina Käser Jacober

Detecting learners in need of support is a complex process for both teachers and machines. Most prior work has devised visualization tools that allow teachers to do so by analyzing educational indicators. Other recent efforts have been devoted to models th ...

ASSOC COMPUTING MACHINERY2023

Explainable ensemble learning model for predicting steel section-concrete bond strength

Xianlin Wang

This study evaluates the efficiency of an explainable ensemble learning framework in precisely predicting the bond strength between steel sections with different surface treatments and various concrete types. Besides seven numerical features, two categoric ...

2022

Use of machine learning for unraveling hidden correlations between particle size distributions and the mechanical behavior of granular materials

Pablo Antolin Sanchez

A data-driven framework was used to predict the macroscopic mechanical behavior of dense packings of polydisperse granular materials. The discrete element method, DEM, was used to generate 92,378 sphere packings that covered many different kinds of particl ...

SPRINGER HEIDELBERG2022

Learning to See and Compute through Multimode Fibers

Demetri Psaltis, Christophe Moser, Damien Claude-Marie Loterie, Eirini Kakkava, Babak Rahmani, Ugur Tegin, Ilker Oguz, Navid Borhani, Mustafa Yildirim

We propose a computational method for controlling the output of a multimode fiber using machine learning. Arbitrary images can be projected with amplitude-only calibration (no phase measurement) and fidelities on par with conventional full-measurement meth ...

IEEE2021

Divide-and-Rule: Self- Supervised Learning for Survival Analysis in Colorectal Cancer

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Christian Robert Abbet

2020

Hydraulic-hydrologic model for the Zambezi River using satellite data and artificial intelligence techniques

José Pedro Gamito de Saldanha Calado Matos

EPFL2014