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Related publications (29)

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Optimal regimes for algorithm-assisted human decision-making

Mats Julius Stensrud, Aaron Leor Sarvet, Julien David Laurendeau

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 ...

2024

Reliable data-driven decision-making through optimal transport

Bahar Taskesen

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

EPFL2024

Machine learning-aided generative molecular design

Philippe Schwaller, Jeff Guo

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Nature Portfolio2024

Contextual Stochastic Bilevel Optimization

Daniel Kuhn, Andreas Krause, Yifan Hu

We introduce contextual stochastic bilevel optimization (CSBO) -- a stochastic bilevel optimization framework with the lower-level problem minimizing an expectation conditioned on some contextual information and the upper-level decision variable. This fram ...

2023

Decision support for the design of integrated biorefineries

Julia Granacher

A shift from fossil-based energy and products to more sustainable alternatives is essential to reduce greenhouse gas emissions and associated climate change impacts. Biomass represents a promising alternative for providing fuels and carbon-based products w ...

EPFL2023

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

Endogenous Energy Efficiency Improvement in Housing and Cement Sectors in Switzerland

Sergey Arzoyan

The reduction of energy consumption in the residential building stock and cement industry is a substantial component of the Swiss Energy Strategy 2050. Therefore, it is vital to identify the potential for EEI in these two sectors. In this study, a new meth ...

EPFL2023

Methodology for selecting measurement points that optimize information gain for model updating

Ian Smith, Numa Joy Bertola

Information collected through sensor measurements has the potential to improve knowledge of complex-system behavior, leading to better decisions related to system management. In this situation, and particularly when using digital twins, the quality of sens ...

2023

Demand-based operations of vehicle sharing systems

Selin Ataç

Vehicle sharing systems (VSSs) allow users to rent vehicles for a short period of time, in a more flexible and convenient manner compared to the traditional vehicle rental services. The long-term VSS subscription replaces the need for contract signing for ...

EPFL2023

On the Validity of Consensus

Rachid Guerraoui, Seth Gilbert, Jovan Komatovic, Manuel José Ribeiro Vidigueira, Pierre Philippe Civit

The Byzantine consensus problem involves.. processes, out of which t < n could be faulty and behave arbitrarily. Three properties characterize consensus: (1) termination, requiring correct (nonfaulty) processes to eventually reach a decision, (2) agreement ...

Assoc Computing Machinery2023

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

Relatively Robust Decisions

Thomas Alois Weber

It is natural for humans to judge the outcome of a decision under uncertainty as a percentage of an ex-post optimal performance. We propose a robust decision-making framework based on a relative performance index. It is shown that if the decision maker’s p ...

2023

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

François Maréchal, Julia Granacher, Tuong-Van Nguyen

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 ...

2022

Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs

Tanja Christina Käser Jacober, Mirko Marras, Vinitra Swamy, Bahar Radmehr

Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainability of their decisions, especially relevant in human-centric fields. We im ...

2022

Use of space analog missions as an educational tool in primary schools

Volker Gass

Space exploration is a field that inspires a wide range of people, from children to university students, including the general public. With a return to the Moon planned for 2024 and the rapid expansion of the private industry, educating the next generation ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Safety Decision-Making for Laboratory Processes in Academia

Thierry Meyer, Anastasia Jung

There is a need for a tool that facilitates safety decision-making in the academic environment. As this environment is very different from that of industry or other public sectors, there is no information available on the factors that influence the decisio ...

2022

Robustness and invariance properties of image classifiers

Apostolos Modas

Deep neural networks have achieved impressive results in many image classification tasks. However, since their performance is usually measured in controlled settings, it is important to ensure that their decisions remain correct when deployed in noisy envi ...

EPFL2022

A prescriptive Dirichlet power allocation policy with deep reinforcement learning

Olga Fink

Prescribing optimal operation based on the condition of the system, and thereby potentially prolonging its remaining useful lifetime, has tremendous potential in terms of actively managing the availability, maintenance, and costs of complex systems. Reinfo ...

2022

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Matthieu Wyart, Antonio Sclocchi, Umberto Maria Tomasini

Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has a 'spectral bias': decomposing the true function f* on the eigenbasis of the ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021

Ekaterina Krymova

Background During the COVID-19 pandemic there has been a strong interest in forecasts of the short-term development of epidemiological indicators to inform decision makers. In this study we evaluate probabilistic real-time predictions of confirmed cases an ...

Springernature2022