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Found 14 papers, 2 papers with code
Fairness in Submodular Maximization over a Matroid Constraint
Submodular maximization over a matroid constraint is a fundamental problem with various applications in machine learning.
Fully Dynamic Submodular Maximization over Matroids
Maximizing monotone submodular functions under a matroid constraint is a classic algorithmic problem with multiple applications in data mining and machine learning.
Fairness in Streaming Submodular Maximization over a Matroid Constraint
Streaming submodular maximization is a natural model for the task of selecting a representative subset from a large-scale dataset.
Deletion Robust Non-Monotone Submodular Maximization over Matroids
Maximizing a submodular function is a fundamental task in machine learning and in this paper we study the deletion robust version of the problem under the classic matroids constraint.
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.
Deletion Robust Submodular Maximization over Matroids
Maximizing a monotone submodular function is a fundamental task in machine learning.
Parallel and Efficient Hierarchical k-Median Clustering
In this paper we introduce a new parallel algorithm for the Euclidean hierarchical $k$-median problem that, when using machines with memory $s$ (for $s\in \Omega(\log^2 (n+\Delta+d))$), outputs a hierarchical clustering such that for every fixed value of $k$ the cost of the solution is at most an $O(\min\{d, \log n\} \log \Delta)$ factor larger in expectation than that of an optimal solution.
Correlation Clustering in Constant Many Parallel Rounds
Correlation clustering is a central topic in unsupervised learning, with many applications in ML and data mining.
Streaming Belief Propagation for Community Detection
The community detection problem requires to cluster the nodes of a network into a small number of well-connected "communities".
Fast and Accurate $k$-means++ via Rejection Sampling
$k$-means++ \cite{arthur2007k} is a widely used clustering algorithm that is easy to implement, has nice theoretical guarantees and strong empirical performance.
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.
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.
Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams
It is the first low-memory, singlepass 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.
