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  • title: Correlation Clustering with Asymmetric Classification Errors
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            Correlation Clustering with Asymmetric Classification Errors
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            Correlation Clustering with Asymmetric Classification Errors

            12. července 2020

            Řečníci

            JJ

            Jafar Jafarov

            Sprecher:in · 0 Follower:innen

            SK

            Sanchit Kalhan

            Sprecher:in · 0 Follower:innen

            KM

            Konstantin Makarychev

            Sprecher:in · 0 Follower:innen

            O prezentaci

            In the Correlation Clustering problem, we are given a weighted graph G with its edges labelled as "similar" or "dissimilar" by a binary classifier. The goal is to produce a clustering that minimizes the weight of "disagreements": the sum of the weights of "similar" edges across clusters and "dissimilar" edges within clusters. We study the correlation clustering problem under the following assumption: Every "similar" edge e has weight w_e ∈ [ α w, w ] and every "dissimilar" edge e has weight w_e…

            Organizátor

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            I2

            ICML 2020

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            Kategorie

            Mathematik

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            KI und Datenwissenschaft

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            O organizátorovi (ICML 2020)

            The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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