Publications related to 1st International Workshop on User-Centered Practices of Knowledge Discovery in Educational Data

Related publications (24)

Image steganalysis using active learning and hyperparameter optimization

Image steganalysis, detecting hidden data in digital images, is essential for enhancing digital security. Traditional steganalysis methods typically rely on large, pre-labeled image datasets, which are difficult and costly to compile. To address this, this ...

Springer Science and Business Media LLC2025

Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

Jérôme Chenal, Baraka Jean-Claude Munyaka

This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (US ...

AIP Publishing2025

Combining physics-based and data-driven models: advancing the frontiers of research with scientific machine learning

Alfio Quarteroni

Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based models rely on the physical understanding of the problem at ...

2025

A journey toward generalizable trajectory forecasting models

Mohammadhossein Bahari

Autonomous driving is a revolutionary technology that has seen considerable advancements through the adoption of deep learning solutions. One of the major challenges in this field is the interaction with other road users. This interaction necessitates a "t ...

EPFL2024

DATA-DRIVEN INVERSE DESIGN METHOD FOR TURBOMACHINERY

Fernando Perez Cruz, Luis Salamanca Miño, Firat Ozdemir

A data-driven inverse design method based on neural networks is proposed for turbomachinery. In the devised methodology, design parameters are provided as input to the neural network, and performance attributes, e.g. efficiency, as output. Once trained, th ...

American Society of Mechanical Engineers (ASME)2024

Distributed Adaptive Learning Under Communication Constraints

Ali H. Sayed

We consider a network of agents that must solve an online optimization problem from continual observation of streaming data. To this end, the agents implement a distributed cooperative strategy where each agent is allowed to perform local exchange of infor ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Learning to Boost the Performance of Stable Nonlinear Systems

Giancarlo Ferrari Trecate, Luca Furieri, Clara Lucía Galimberti

The growing scale and complexity of safety-critical control systems underscore the need to evolve current control architectures aiming for the unparalleled performances achievable through state-of-the-art optimization and machine learning algorithms. Howev ...

2024

Hyperdimensional computing with holographic and adaptive encoder

Alejandro Hernández Cano

Introduction: Brain-inspired computing has become an emerging field, where a growing number of works focus on developing algorithms that bring machine learning closer to human brains at the functional level. As one of the promising directions, Hyperdimensi ...

2024

Decoding cerebro-spinal signatures of human behavior: Application to motor sequence learning

Dimitri Nestor Alice Van De Ville, Nawal Noëlle Kinany

Mapping the neural patterns that drive human behavior is a key challenge in neuroscience. Even the simplest of our everyday actions stem from the dynamic and complex interplay of multiple neural structures across the central nervous system (CNS). Yet, most ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2023

Protected Attributes Tell Us Who, Behavior Tells Us How: A Comparison of Demographic and Behavioral Oversampling for Fair Student Success Modeling

Tanja Christina Käser Jacober

Algorithms deployed in education can shape the learning experience and success of a student. It is therefore important to understand whether and how such algorithms might create inequalities or amplify existing biases. In this paper, we analyze the fairnes ...

2022

Generalisable Methods for Early Prediction in Interactive Simulations for Education

Tanja Christina Käser Jacober, Christian Giang, Jade Maï L Cock

Interactive simulations allow students to discover the underlying principles of a scientific phenomenon through their own exploration. Unfortunately, students often struggle to learn effectively in these environments. Classifying students’ interaction data ...

2022

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

Andreas Krause, Maryam Kamgarpour

We consider model-based multi-agent reinforcement learning, where the environment transition model is unknown and can only be learned via expensive interactions with the environment. We propose H-MARL (Hallucinated Multi-Agent Reinforcement Learning), a no ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

An End-to-End Data Pipeline for Managing Learning Analytics

Denis Gillet, Juan Carlos Farah, Sandy Ingram, Joana Catarina Soares Machado

Despite the importance of learning analytics in digital education, there is limited support for researchers in education to generate, access, and share experimental data while complying with ethical and privacy legislation. We propose a set of related tool ...

2021

Plasma confinement mode classification using a sequence-to-sequence neural network with attention

Alessandro Pau, Gino Marceca

In a typical fusion experiment, the plasma can have several possible confinement modes. At the tokamak a configuration variable, aside from the low (L) and high (H) confinement modes, an additional mode, dithering (D), is frequently observed. Developing me ...

IOP Publishing Ltd2021

Representation Learning for Multi-relational Data

Eda Bayram

Recent years have witnessed a rise in real-world data captured with rich structural information that can be better depicted by multi-relational or heterogeneous graphs. However, research on relational representation learning has so far mostly focused on th ...

EPFL2021

Graph-Homomorphic Perturbations For Private Decentralized Learning

Ali H. Sayed, Stefan Vlaski

Decentralized algorithms for stochastic optimization and learning rely on the diffusion of information through repeated local exchanges of intermediate estimates. Such structures are particularly appealing in situations where agents may be hesitant to shar ...

IEEE2021

Social NCE: Contrastive Learning of Socially-aware Motion Representations

Alexandre Massoud Alahi, Yuejiang Liu

Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds. Despite promising progress, existing representations learned with neural networks sti ...

2021

Adaptive Laser Welding Control: A Reinforcement Learning Approach

Giulio Masinelli, Silvio Zanoli

Despite extensive research efforts in the field of laser welding, the imperfect repeatability of the weld quality still represents an open topic. Indeed, the inherent complexity of the underlying physical phenomena prevents the implementation of an effecti ...

2020

Application of machine learning techniques at the CERN Large Hadron Collider

Frédéric Blanc, Tatiana Pieloni, Michael Schenk, Leonid Rivkin, Loïc Thomas Davies Coyle

Machine learning techniques have been used extensively in several domains of Science and Engineering for decades. These powerful tools have been applied also to the domain of high-energy physics, in the analysis of the data from particle collisions, for ye ...

Trieste2020

On Vacuous and Non-Vacuous Generalization Bounds for Deep Neural Networks

Konstantinos Pitas

Deep neural networks have been empirically successful in a variety of tasks, however their theoretical understanding is still poor. In particular, modern deep neural networks have many more parameters than training data. Thus, in principle they should over ...

EPFL2020