-
Optimal Approximation – Smoothness Tradeoffs for Soft-Max Functions
A soft-max function has two main efficiency measures: (1) approximation ...
10/22/2020 ∙ by Alessandro Epasto, et al. ∙
0
∙
share
read it
-
Fair Hierarchical Clustering
As machine learning has become more prevalent, researchers have begun to...
06/18/2020 ∙ by Sara Ahmadian, et al. ∙
0
∙
share
read it
-
Sliding Window Algorithms for k-Clustering Problems
The sliding window model of computation captures scenarios in which data...
06/10/2020 ∙ by Michele Borassi, et al. ∙
0
∙
share
read it
-
Fair Correlation Clustering
In this paper, we study correlation clustering under fairness constraint...
02/06/2020 ∙ by Sara Ahmadian, et al. ∙
0
∙
share
read it
-
Bisect and Conquer: Hierarchical Clustering via Max-Uncut Bisection
Hierarchical Clustering is an unsupervised data analysis method which ha...
12/15/2019 ∙ by Sara Ahmadian, et al. ∙
0
∙
share
read it
-
Clustering without Over-Representation
In this paper we consider clustering problems in which each point is end...
05/29/2019 ∙ by Sara Ahmadian, et al. ∙
0
∙
share
read it
-
Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts
Recent interest in graph embedding methods has focused on learning a sin...
05/06/2019 ∙ by Alessandro Epasto, et al. ∙
0
∙
share
read it
-
On a Model for Integrated Information
In this paper we give a thorough presentation of a model proposed by Ton...
12/31/2009 ∙ by Alessandro Epasto, et al. ∙
0
∙
share
read it
