Concept
Inférence causale
Publications associées (127)
Modulation of Visually Induced Self-motion Illusions by α Transcranial Electric Stimulation over the Superior Parietal Cortex
The growing popularity of virtual reality systems has led to a renewed interest in understanding the neurophysiological correlates of the illusion of self-motion (vection), a phenomenon that can be both intentionally induced or avoided in such systems, dep ...
Cambridge2024Acoustical Features as Knee Health Biomarkers: A Critical Analysis
David Atienza Alonso, Vincent Stadelmann, Tomas Teijeiro Campo, Jérôme Paul Rémy Thevenot, Christodoulos Kechris
Acoustical knee health assessment has long promised an alternative to clinically available medical imaging tools, but this modality has yet to be adopted in medical practice. The field is currently led by machine learning models processing acoustical featu ...
2024Using Negative Control Populations to Assess Unmeasured Confounding and Direct Effects
Sometimes treatment effects are absent in a subgroup of the population. For example, penicillin has no effect on severe symptoms in individuals infected by resistant Staphylococcus aureus, and codeine has no effect on pain in individuals with certain polym ...
Lippincott Williams & Wilkins2024Dynamic Voxels Based on Ego-Conditioned Prediction: An Integrated Spatio-Temporal Framework for Motion Planning
Alexandre Massoud Alahi, Ting Zhang
Prediction is a vital component of motion planning for autonomous vehicles (AVs). By reasoning about the possible behavior of other target agents, the ego vehicle (EV) can navigate safely, efficiently, and politely. However, most of the existing work overl ...
Ieee-Inst Electrical Electronics Engineers Inc2024Content Moderation in Online Platforms
A critical role of online platforms like Facebook, Wikipedia, YouTube, Amazon, Doordash, and Tinder is to moderate content. Interventions like banning users or deleting comments are carried out thousands of times daily and can potentially improve our onlin ...
EPFL2024Exploiting the Signal-Leak Bias in Diffusion Models
Sabine Süsstrunk, Radhakrishna Achanta, Mahmut Sami Arpa, Martin Nicolas Everaert, Athanasios Fitsios
There is a bias in the inference pipeline of most diffusion models. This bias arises from a signal leak whose distribution deviates from the noise distribution, creating a discrepancy between training and inference processes. We demonstrate that this signa ...
2024Optimal regimes for algorithm-assisted human decision-making
Mats Julius Stensrud, Aaron Leor Sarvet
We consider optimal regimes for algorithm-assisted human decision-making. Such regimes are decision functions of measured pre-treatment variables and, by leveraging natural treatment values, enjoy a superoptimality property whereby they are guaranteed to o ...
2024Causal Influences over Social Learning Networks
This paper investigates causal influences between agents linked by a social graph and interacting over time. In particular, the work examines the dynamics of social learning models and distributed decision-making protocols, and derives expressions that rev ...
2023Distribution 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