Evaluating the predictive abilities of mixed logit models with unobserved inter- and intra-individual heterogeneity

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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

Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UK

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Smartphones enable understanding human behavior with activity recognition to support peoples daily lives. Prior studies focused on using inertial sensors to detect simple activities (sitting, walking, running, etc.) and were mostly conducted in homogeneous ...

Assoc Computing Machinery2023

Exploration-based model learning with self-attention for risk-sensitive robot control

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Model-based reinforcement learning for robot control offers the advantages of overcoming concerns on data collection and iterative processes for policy improvement in model-free methods. However, both methods use exploration strategy relying on heuristics ...

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Decision Procedures for Power Structures

Rodrigo Raya

We study the decision problem for the existential fragment of the theory of power structures. We prove complexity results that parallel the decidability results of Feferman-Vaught for the theories of product structures thereby showing that the construction ...

EPFL2023

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Emilio Maddalena

EPFL2023

Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UK

Daniel Gatica-Perez, Lakmal Buddika Meegahapola, Karim Assi

Assoc Computing Machinery2023

Exploration-based model learning with self-attention for risk-sensitive robot control

Sudong Lee

2023

Safety risk management in University laboratories

Anastasia Jung

Risk management has become an essential element in the functioning of modern society. Correct risk identification and assessment are undoubtedly crucial to improving overall safety; nevertheless, often, it is accompanied by the wrong selection of correctiv ...

EPFL2023

A mixed precision LOBPCG algorithm

Daniel Kressner, Meiyue Shao, Yuxin Ma

The locally optimal block preconditioned conjugate gradient (LOBPCG) algorithm is a popular approach for computing a few smallest eigenvalues and the corresponding eigenvectors of a large Hermitian positive definite matrix A. In this work, we propose a mix ...

SPRINGER2023

Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning

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Predictive models based on machine learning (ML) offer a compelling promise: bringing clarity and structure to complex natural and social environments. However, the use of ML poses substantial risks related to the privacy of their training data as well as ...

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Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU

Nahal Mansouri, Sahand Jamal Rahi

Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies.Objecti ...

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Overcoming decision paralysis—A digital twin for decision making in energy system design

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The design of efficient energy systems, through the development of new technologies and the improvement of current ones, requires the use of rigorous process synthesis methods for generating and analysing design alternatives. We introduce a digital twin of ...

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