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Found 9 papers, 0 papers with code
Structural perspective on constraint-based learning of Markov networks
When it comes to the number of tests, our upper bound on the sizes of conditioning sets implies that every $n$-vertex graph can be learned by at most $n^{\kappa}$ tests with conditioning sets of sizes at most $\kappa$.
How to Find a Good Explanation for Clustering?
(2) For a given set of points, how to find a decision tree with $k$ leaves minimizing the $k$-means/median objective of the resulting explainable clustering?
Can Romeo and Juliet Meet? Or Rendezvous Games with Adversaries on Graphs
Facilitator wins if his agents meet in some vertex of the graph.
Discrete Mathematics Computational Complexity Data Structures and Algorithms 05C85 G.2.2
Diverse Collections in Matroids and Graphs
The input to the Weighted Diverse Bases problem consists of a matroid $M$, a weight function $\omega:E(M)\to\mathbb{N}$, and integers $k\geq 1, d\geq 0$.
Data Structures and Algorithms
Present-Biased Optimization
This paper explores the behavior of present-biased agents, that is, agents who erroneously anticipate the costs of future actions compared to their real costs.
EPTAS for $k$-means Clustering of Affine Subspaces
We consider a generalization of the fundamental $k$-means clustering for data with incomplete or corrupted entries.
On Coresets for Fair Clustering in Metric and Euclidean Spaces and Their Applications
The new construction allows us to obtain the first coreset for fair clustering in general metric spaces.
Refined Complexity of PCA with Outliers
Principal component analysis (PCA) is one of the most fundamental procedures in exploratory data analysis and is the basic step in applications ranging from quantitative finance and bioinformatics to image analysis and neuroscience.
Approximation Schemes for Low-Rank Binary Matrix Approximation Problems
The new constrained clustering problem encompasses a number of problems and by solving it, we obtain the first linear time-approximation schemes for a number of well-studied fundamental problems concerning clustering of binary vectors and low-rank approximation of binary matrices.
