Publications associées à Determining the number of clusters in a data set

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

Fairness and Explainability in Clustering Problems

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In this thesis we present and analyze approximation algorithms for three different clustering problems. The formulations of these problems are motivated by fairness and explainability considerations, two issues that have recently received attention in the ...

EPFL2023

Transfer learning application of self-supervised learning in ARPES

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There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lase ...

IOP Publishing Ltd2023

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

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Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled ...

Assoc Computing Machinery2023

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

Single-Trace Clustering Power Analysis of the Point-Swapping Procedure in the Three Point Ladder of Cortex-M4 SIKE

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Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap

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A simple model to study subspace clustering is the high-dimensional k -Gaussian mixture model where the cluster means are sparse vectors. Here we provide an exact asymptotic characterization of the statistically optimal reconstruction error in this model i ...

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Covariance matrices for variance-suppressed simulations

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Cosmological N-body simulations provide numerical predictions of the structure of the Universe against which to compare data from ongoing and future surveys, but the growing volume of the Universe mapped by surveys requires correspondingly lower statistica ...

OXFORD UNIV PRESS2022

Binary Perceptron: Efficient Algorithms Can Find Solutions in a RareWell-Connected Cluster

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It was recently shown that almost all solutions in the symmetric binary perceptron are isolated, even at low constraint densities, suggesting that finding typical solutions is hard. In contrast, some algorithms have been shown empirically to succeed in fin ...

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Linguistic traits as heritable units? Spatial Bayesian clustering reveals Swiss German dialect regions

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In the early 2000s, the SADS, an extensive linguistic atlas project, surveyed more than three thousand individuals across German-speaking Switzerland on over two hundred linguistic variants, capturing the morphosyntactic variation in Swiss German. In this ...

2022