Publications associées à Modeling and Analyzing Inquiry Strategies in Open-Ended Learning Environments

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

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Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

2024

Social Opinion Formation and Decision Making Under Communication Trends

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This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...

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EPFL2024

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Extensions of Peer Prediction Incentive Mechanisms

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As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

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Informative sample selection in an active learning (AL) setting helps a machine learning system attain optimum performance with minimum labeled samples, thus reducing annotation costs and boosting performance of computer-aided diagnosis systems in the pres ...

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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 ...

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Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

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Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

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

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In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

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Robust machine learning for neuroscientific inference

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Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

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Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

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Resource-Efficient Continual Learning for Personalized Online Seizure Detection

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Students learn more when they are actively engaged in the learning process. While hands-on activities, labs and projects are moments when students are active, the learning benefits can be amplified with coaching strategies. This activity will enable studen ...

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Transfer learning application of self-supervised learning in ARPES

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Fairness and Explainability in Clustering Problems

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Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining

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This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions and arising from possibly different distributions. In the context of social learning ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Co-constructing an Effective, Sustainable and Scalable Primary School Computer Science Curricular Reform

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Over the past few decades, the debates have shifted from whether to how Computer Science (CS) should be introduced into formal education. Given the diverse ways to introduce CS into formal education, and the struggles many countries have faced, considerabl ...

EPFL2023