Concept

Biais de confirmation

Publications associées (119)

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

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

Serial Dependence in Human Visual Perception and Decision-Making

Gizay Ceylan

Decisions about a current visual stimulus are systematically biased by recently encountered stimuli, a phenomenon known as serial dependence. In human vision, for instance, we tend to report the features of current images as more similar â i.e., an attra ...

EPFL2024

Hybrid Simulator for Capturing Dynamics of Synthetic Populations

Michel Bierlaire, Marija Kukic

This paper presents a novel hybrid framework for generating and updating a synthetic population. We call it hybrid because it combines model-based and data-driven approaches. Existing generators produce a snapshot of synthetic data that becomes outdated ov ...

IEEE2024

Unraveling ice multiplication in winter orographic clouds via in-situ observations, remote sensing and modeling

Athanasios Nenes, Alexis Berne, Satoshi Takahama, Georgia Sotiropoulou, Paraskevi Georgakaki, Romanos Foskinis, Kunfeng Gao, Anne-Claire Marie Billault--Roux

Recent years have shown that secondary ice production (SIP) is ubiquitous, affecting all clouds from polar to tropical regions. SIP is not described well in models and may explain biases in warm mixed-phase cloud ice content and structure. Through modeling ...

Nature Portfolio2024

The inductive bias of deep learning: Connecting weights and functions

Guillermo Ortiz Jimenez

Years of a fierce competition have naturally selected the fittest deep learning algorithms. Yet, although these models work well in practice, we still lack a proper characterization of why they do so. This poses serious questions about the robustness, trus ...

EPFL2023

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

Potential-induced degradation in bifacial silicon heterojunction solar modules: Insights and mitigation strategies

Christophe Ballif, Quentin Thomas Jeangros, Alessandro Francesco Aldo Virtuani, Olatz Arriaga Arruti, Luca Gnocchi

Potential-induced degradation (PID) may be a serious concern in photovoltaic (PV) modules and plants, particularly when approaching high system voltages (1500+ V). Here, we investigate PID occurring in bifacial rear-emitter silicon heterojunction (SHJ) sol ...

Hoboken2023

Rethinking data augmentation for adversarial robustness

Kathrin Grosse

Recent work has proposed novel data augmentation methods to improve the adversarial robustness of deep neural networks. In this paper, we re-evaluate such methods through the lens of different metrics that characterize the augmented manifold, finding contr ...

2023

Information Processing Biases: The Effects of Negative Emotional Symptoms on Sampling Pleasant and Unpleasant Information

Steffen Alexander Herff

Although theories of emotion associate negative emotional symptoms with cognitive biases in information processing, they rarely specify the details. Here, we characterize cognitive biases in information processing of pleasant and unpleasant information, an ...

AMER PSYCHOLOGICAL ASSOC2022

Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling

Vinitra Swamy, Thiemo Wambsganss

Natural Language Processing (NLP) has become increasingly utilized to provide adaptivity in educational applications. However, recent research has highlighted a variety of biases in pre-trained language models. While existing studies investigate bias in di ...

2022

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