Search Results for author:
Found 13 papers, 1 papers with code
Fair Representation Clustering with Several Protected Classes
For this special case, we present an $O(\log k)$-approximation algorithm that runs in $(kf)^{O(\ell)}\log n + poly(n)$ time.
Approximating Fair Clustering with Cascaded Norm Objectives
We utilize convex programming techniques to approximate the $(p, q)$-Fair Clustering problem for different values of $p$ and $q$.
Local Correlation Clustering with Asymmetric Classification Errors
In the Correlation Clustering problem, we are given a complete weighted graph $G$ with its edges labeled as "similar" and "dissimilar" by a noisy binary classifier.
Correlation Clustering with Asymmetric Classification Errors
In the Correlation Clustering problem, we are given a weighted graph $G$ with its edges labeled as "similar" or "dissimilar" by a binary classifier.
Approximation Algorithms for Socially Fair Clustering
In order to obtain our result, we introduce a strengthened LP relaxation and show that it has an integrality gap of $\Theta(\frac{\log \ell}{\log\log\ell})$ for a fixed $p$.
Efficient Kirszbraun Extension with Applications to Regression
We introduce a framework for performing regression between two Hilbert spaces.
Nonlinear Dimension Reduction via Outer Bi-Lipschitz Extensions
We introduce and study the notion of an outer bi-Lipschitz extension of a map between Euclidean spaces.
Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering
Further, the cost of every clustering is preserved within $(1+\varepsilon)$.
Learning Communities in the Presence of Errors
Many algorithms exist for learning communities in the Stochastic Block Model, but they do not work well in the presence of errors.
Constant Factor Approximation for Balanced Cut in the PIE model
Let $G$ be an arbitrary graph on $V$ with no edges between $L$ and $R$.
Correlation Clustering with Noisy Partial Information
In this paper, we propose and study a semi-random model for the Correlation Clustering problem on arbitrary graphs G. We give two approximation algorithms for Correlation Clustering instances from this model.
Clustering, Hamming Embedding, Generalized LSH and the Max Norm
We study the convex relaxation of clustering and hamming embedding, focusing on the asymmetric case (co-clustering and asymmetric hamming embedding), understanding their relationship to LSH as studied by (Charikar 2002) and to the max-norm ball, and the differences between their symmetric and asymmetric versions.
The Power of Asymmetry in Binary Hashing
When approximating binary similarity using the hamming distance between short binary hashes, we show that even if the similarity is symmetric, we can have shorter and more accurate hashes by using two distinct code maps.
