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Found 9 papers, 2 papers with code
Near-Optimal Correlation Clustering with Privacy
Correlation clustering is a central problem in unsupervised learning, with applications spanning community detection, duplicate detection, automated labelling and many more.
Correlation Clustering in Constant Many Parallel Rounds
Correlation clustering is a central topic in unsupervised learning, with many applications in ML and data mining.
Fairness in Streaming Submodular Maximization: Algorithms and Hardness
Submodular maximization has become established as the method of choice for the task of selecting representative and diverse summaries of data.
Online Page Migration with ML Advice
In contrast, we show that if the algorithm is given a prediction of the input sequence, then it can achieve a competitive ratio that tends to $1$ as the prediction error rate tends to $0$.
Massively Parallel Algorithms for Small Subgraph Counting
Finally, we prove that a recent result of Bera, Pashanasangi and Seshadhri (ITCS 2020) for exactly counting all subgraphs of size at most $5$ can be implemented in the MPC model in total space.
Data Structures and Algorithms Distributed, Parallel, and Cluster Computing
Adversarially Robust Submodular Maximization under Knapsack Constraints
We propose the first adversarially robust algorithm for monotone submodular maximization under single and multiple knapsack constraints with scalable implementations in distributed and streaming settings.
Beyond $1/2$-Approximation for Submodular Maximization on Massive Data Streams
It is the first low-memory, single-pass algorithm that improves the factor $0. 5$, under the natural assumption that elements arrive in a random order.
Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach
We study the classical problem of maximizing a monotone submodular function subject to a cardinality constraint k, with two additional twists: (i) elements arrive in a streaming fashion, and (ii) m items from the algorithm's memory are removed after the stream is finished.
Robust Submodular Maximization: A Non-Uniform Partitioning Approach
We study the problem of maximizing a monotone submodular function subject to a cardinality constraint $k$, with the added twist that a number of items $\tau$ from the returned set may be removed.
