Publications associées à INFORMATION THEORETIC CLUSTERING FOR UNSUPERVISED DOMAIN-ADAPTATION

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Vinitra Swamy, Paola Mejia Domenzain, Julian Thomas Blackwell, Isadora Alves de Salles

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

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

Yichen Xu, Qiang Liu, Feng Yu

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 ...

New York2023

Unsupervised Electrofacies Clustering Based on Parameterization of Petrophysical Properties: A Dynamic Programming Approach

François Fleuret, Karthigan Sinnathamby

Electrofacies using well logs play a vital role in reservoir characterization. Often, they are sorted into clusters according to the self-similarity of input logs and do not capture the known underlying physical process. In this paper, we propose an unsupe ...

SOC PETROPHYSICISTS & WELL LOG ANALYSTS-SPWLA2023

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Michael Christoph Gastpar, Amedeo Roberto Esposito, Ibrahim Issa

In this work, the probability of an event under some joint distribution is bounded by measuring it with the product of the marginals instead (which is typically easier to analyze) together with a measure of the dependence between the two random variables. ...

2021

Nearly-Tight and Oblivious Algorithms for Explainable Clustering

Ola Nils Anders Svensson, Adam Teodor Polak, Buddhima Ruwanmini Gamlath Gamlath Ralalage, Xinrui Jia

We study the problem of explainable clustering in the setting first formalized by Dasgupta, Frost, Moshkovitz, and Rashtchian (ICML 2020). A k-clustering is said to be explainable if it is given by a decision tree where each internal node splits data point ...

2021

Spherical Clustering of Users Navigating 360° Content

Pascal Frossard, Francesca De Simone, Laura Toni

In Virtual Reality (VR) applications, understanding how users explore the omnidirectional content is important to optimize content creation, to develop user-centric services, or even to detect disorders in medical applications. Clustering users based on th ...

IEEE2019

Predicting polymorphism in molecular crystals using orientational entropy

Pablo Miguel Piaggi, Michele Parrinello

We introduce a computational method to discover polymorphs in molecular crystals at finite temperature. The method is based on reproducing the crystallization process starting from the liquid and letting the system discover the relevant polymorphs. This id ...

2018

epsilon-Coresets for Clustering (with Outliers) in Doubling Metrics

Jian Li, Lingxiao Huang

We study the problem of constructing epsilon-coresets for the (k, z)-clustering problem in a doubling metric M(X, d). An epsilon-coreset is a weighted subset S subset of X with weight function w : S -> R->= 0, such that for any k-subset C is an element of ...

IEEE COMPUTER SOC2018

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INFORMATION THEORETIC CLUSTERING FOR UNSUPERVISED DOMAIN-ADAPTATION

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Fairness and Explainability in Clustering Problems

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

Unsupervised Electrofacies Clustering Based on Parameterization of Petrophysical Properties: A Dynamic Programming Approach

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Nearly-Tight and Oblivious Algorithms for Explainable Clustering

On Perfect Clustering of High Dimension, Low Sample Size Data

Spherical Clustering of Users Navigating 360° Content

Predicting polymorphism in molecular crystals using orientational entropy

epsilon-Coresets for Clustering (with Outliers) in Doubling Metrics

Graph Chatbot

Chattez avec Graph Search

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

Unsupervised Electrofacies Clustering Based on Parameterization of Petrophysical Properties: A Dynamic Programming Approach

Predicting polymorphism in molecular crystals using orientational entropy

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Fairness and Explainability in Clustering Problems

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Nearly-Tight and Oblivious Algorithms for Explainable Clustering

On Perfect Clustering of High Dimension, Low Sample Size Data

Spherical Clustering of Users Navigating 360° Content

epsilon-Coresets for Clustering (with Outliers) in Doubling Metrics