Publication

Predictably Unequal? The Effects of Machine Learning on Credit Markets

Publications associées (29)

Predicting Survey Response with Quotation-based Modeling: A Case Study on Favorability towards the United States

Saleh Baghersalimi, Jonathan Philippe Reymond, Alireza Amirshahi

The acquisition of survey responses is a crucial component in conducting research aimed at comprehending public opinion. However, survey data collection can be arduous, time-consuming, and expensive, with no assurance of an adequate response rate. In this ...

IEEE COMPUTER SOC2023

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Emilio Maddalena

This thesis is situated at the crossroads between machine learning and control engineering. Our contributions are both theoretical, through proposing a new uncertainty quantification methodology in a kernelized context; and experimental, through investigat ...

EPFL2023

Out of their minds? Externalist challenges for using AI in forensic psychiatry

Marcello Ienca, Ambra D'imperio

Harnessing the power of machine learning (ML) and other Artificial Intelligence (AI) techniques promises substantial improvements across forensic psychiatry, supposedly offering more objective evaluations and predictions. However, AI-based predictions abou ...

FRONTIERS MEDIA SA2023

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Leonardo Petrini

Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...

EPFL2023

Keep Sensors in Check: Disentangling Country-Level Generalization Issues in Mobile Sensor-Based Models with Diversity Scores

Daniel Gatica-Perez, Lakmal Buddika Meegahapola

Machine learning models trained with passive sensor data from mobile devices can be used to perform various inferences pertaining to activity recognition, context awareness, and health and well-being. Prior work has improved inference performance through t ...

Assoc Computing Machinery2023

Toward data-driven materials design: From atoms to pilot plants

Kevin Maik Jablonka

Discovering new materials is essential but challenging, time-consuming, and expensive. In many cases, simulations can be useful for estimating material properties. For many of the most interesting properties, however, simulations are infeasible because of ...

EPFL2023

Robustness of Local Predictions in Atomistic Machine Learning Models

Michele Ceriotti, Chiheb Ben Mahmoud, Federico Grasselli, Sanggyu Chong

Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven si ...

Amer Chemical Soc2023

Distribution Inference Risks: Identifying and Mitigating Sources of Leakage

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

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

Medical and Personal Characteristics Can Predict the Risk of Lung Metastasis

Michele De Palma, Nahal Mansouri

Aims: Understanding the correlations between underlying medical and personal characteristics of a patient with cancer and the risk of lung metastasis may improve clinical management and outcomes. We used machine learning methodologies to predict the risk o ...

2023