Publication

Identifiability and Generalizability from Multiple Experts in Inverse Reinforcement Learning

Publications associées (30)

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

EPFL2024

Testing Theories of the Glass Transition with the Same Liquid but Many Kinetic Rules

Matthieu Wyart, Carolina Brito Carvalho dos Santos

We study the glass transition by exploring a broad class of kinetic rules that can significantly modify the normal dynamics of supercooled liquids while maintaining thermal equilibrium. Beyond the usual dynamics of liquids, this class includes dynamics in ...

Amer Physical Soc2024

Unveiling the complexity of learning and decision-making

Wei-Hsiang Lin

Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the prediction error is an important component that compares the expected and actual rewards. Dopamine plays a critical role in encoding these prediction errors. In my ...

EPFL2024

Nanoindentation hardness and modulus of Al2O3-SiO2-CaO and MnO-SiO2-FeO inclusions in iron

Andreas Mortensen, David Hernandez Escobar, Léa Deillon, Alejandra Inés Slagter, Eva Luisa Vogt, Jonathan Aristya Setyadji

Dataset corresponding to the following manuscript: Slagter, A., Setyadji, J.A., Vogt, E.L. et al. Nanoindentation Hardness and Modulus of Al2O3–SiO2–CaO and MnO–SiO2–FeO Inclusions in Iron. Metall Mater Trans A (2024). https://doi.org/10.1007/s11661-024-0 ...

Zenodo2024

Data set for control of Ge island coalescence for the formation of nanowires on silicon

Anna Fontcuberta i Morral, Alok Rudra, Santhanu Panikar Ramanandan, Joel René Sapera, Vladimir Dubrovskii, Sara Marti Sanchez

This document contains all the data and the details of the analysis used in the manuscript titled " Control of Ge island coalescence for the formation of nanowires on silicon." https://doi.org/10.1039/D3NH00573A ...

EPFL Infoscience2024

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Lakmal Buddika Meegahapola

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

EPFL2024

Hybrid Simulator for Capturing Dynamics of Synthetic Populations

Michel Bierlaire, Marija Kukic

This paper presents a novel hybrid framework for generating and updating a synthetic population. We call it hybrid because it combines model-based and data-driven approaches. Existing generators produce a snapshot of synthetic data that becomes outdated ov ...

IEEE2024

Dataset for '3D printing of customizable transient bioelectronics and sensors'

Danick Briand, Nicolas Francis Fumeaux

This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the recent publication entitled “3D printing of customizable transient bioelectronics and sensors”. This work aims to study and demonstrate ...

Zenodo2024

Data and scripts for the RaFSIP scheme

Athanasios Nenes, Paraskevi Georgakaki

This repository contains microphysics routines, scripts, and processed data from the Weather Research and Forecasting (WRF) model simulations presented in the paper "RaFSIP: Parameterizing ice multiplication in models using a machine learning approach", by ...

Zenodo2024

Identifiability and Generalizability from Multiple Experts in Inverse Reinforcement Learning

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Testing Theories of the Glass Transition with the Same Liquid but Many Kinetic Rules

Unveiling the complexity of learning and decision-making

Nanoindentation hardness and modulus of Al2O3-SiO2-CaO and MnO-SiO2-FeO inclusions in iron

Data set for control of Ge island coalescence for the formation of nanowires on silicon

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Hybrid Simulator for Capturing Dynamics of Synthetic Populations

Dataset for '3D printing of customizable transient bioelectronics and sensors'

Data and scripts for the RaFSIP scheme

Graph Chatbot

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Testing Theories of the Glass Transition with the Same Liquid but Many Kinetic Rules

Unveiling the complexity of learning and decision-making

Nanoindentation hardness and modulus of Al2O3-SiO2-CaO and MnO-SiO2-FeO inclusions in iron

Data set for control of Ge island coalescence for the formation of nanowires on silicon

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Hybrid Simulator for Capturing Dynamics of Synthetic Populations

Dataset for '3D printing of customizable transient bioelectronics and sensors'

Data and scripts for the RaFSIP scheme

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems