Buddhima Ruwanmini Gamlath Gamlath Ralalage

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Related publications (4)

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

How to Match in Modern Computational Settings

Buddhima Ruwanmini Gamlath Gamlath Ralalage

This thesis focuses on the maximum matching problem in modern computational settings where the algorithms have to make decisions with partial information. First, we consider two stochastic models called query-commit and price-of-information where the algo ...

EPFL2021

Online Matching with General Arrivals

Mikhail Kapralov, Ola Nils Anders Svensson, Buddhima Ruwanmini Gamlath Gamlath Ralalage, Andreas Maggiori, David Wajc

The online matching problem was introduced by Karp, Vazirani and Vazirani nearly three decades ago. In that seminal work, they studied this problem in bipartite graphs with vertices arriving only on one side, and presented optimal deterministic and randomi ...

IEEE COMPUTER SOC2019

Weighted Matchings via Unweighted Augmentations

Slobodan Mitrovic, Ola Nils Anders Svensson, Buddhima Ruwanmini Gamlath Gamlath Ralalage

We design a generic method to reduce the task of finding weighted matchings to that of finding short augmenting paths in unweighted graphs. This method enables us to provide efficient implementations for approximating weighted matchings in the massively pa ...

ASSOC COMPUTING MACHINERY2019

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