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Publications associées (31)

Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning

We propose two deep learning models that fully automate shape parameterization for aerodynamic shape optimization. Both models are optimized to parameterize via deep geometric learning to embed human prior knowledge into learned geometric patterns, elimina ...

2023

Learning Robust and Adaptive Representations: from Interactions, for Interactions

Yuejiang Liu

Interactions are ubiquitous in our world, spanning from social interactions between human individuals to physical interactions between robots and objects to mechanistic interactions among different components of an intelligent system. Despite their prevale ...

EPFL2023

Automatic Uniform Quantum State Preparation Using Decision Diagrams

Giovanni De Micheli, Mathias Soeken, Fereshte Mozafari Ghoraba, Heinz Riener

Most quantum algorithms assume some specific initial state in superposition of basis states before performing the desired application-specific computations. The preparation of such states itself requires a computation performed by a quantum circuit. In thi ...

2020

Information Theoretic Characterization of Uncertainty Distinguishes Surprise From Accuracy Signals in the Brain

Kerstin Preuschoff, Leyla Loued-Khenissi

Uncertainty presents a problem for both human and machine decision-making. While utility maximization has traditionally been viewed as the motive force behind choice behavior, it has been theorized that uncertainty minimization may supersede reward motivat ...

2020

Product assortment and space allocation strategies to attract loyal and non-loyal customers

Ralf Seifert, Anna Timonina-Farkas, Argyro Katsifou

Assortment planning deserves much attention from practitioners and academics due to its direct impact on retailers' commercial success. In this paper we focus on the increasingly popular retail practice to use combined product assortments with both "standa ...

2020

Quantification of the conditional value of SHM data for the fatigue safety evaluation of a road viaduct

Eugen Brühwiler, Imane Bayane

Fatigue safety verification of existing bridges that uses ‘‘re-calculation’’ based on codes, usually results in insufficient fatigue safety, triggering invasive interventions. Instead of “re-calculation”, Structural Health Monitoring (SHM) should be used f ...

2019

One-shot learning and behavioral eligibility traces in sequential decision making

Michael Herzog, Wulfram Gerstner, Kerstin Preuschoff, Marco Philipp Lehmann, He Xu, Vasiliki Liakoni

In many daily tasks, we make multiple decisions before reaching a goal. In order to learn such sequences of decisions, a mechanism to link earlier actions to later reward is necessary. Reinforcement learning (RL) theory suggests two classes of algorithms s ...

2019

"Dice"-sion Making under Uncertainty: When Can a Random Decision Reduce Risk?

Daniel Kuhn, Wolfram Wiesemann

Stochastic programming and distributionally robust optimization seek deterministic decisions that optimize a risk measure, possibly in view of the most adverse distribution in an ambiguity set. We investigate under which circumstances such deterministic de ...

2019

Optimal Information Collection Policies in a Markov Decision Process Framework

Thomas Alois Weber

The cost-effectiveness and value of additional information about a health technology or program may change over time because of trends affecting patient cohorts and/or the intervention. Delaying information collection even for parameters that do not change ...

2018

One-shot learning and eligibility traces in sequential decision making

Marco Philipp Lehmann

When humans or animals perform an action that led to a desired outcome, they show a tendency to repeat it. The mechanisms underlying learning from past experience and adapting future behavior are still not fully understood. In this thesis, I study how huma ...

EPFL2018

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