no code implementations • 13 Mar 2024 • Tuukka Korhonen, Fedor V. Fomin, Pekka Parviainen
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 $\kappa$.
no code implementations • 13 Dec 2021 • Sayan Bandyapadhyay, Fedor V. Fomin, Petr A. Golovach, William Lochet, Nidhi Purohit, Kirill Simonov
(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?
no code implementations • 26 Feb 2021 • Fedor V. Fomin, Petr A. Golovach, Dimitrios M. Thilikos
Facilitator wins if his agents meet in some vertex of the graph.
Discrete Mathematics Computational Complexity Data Structures and Algorithms 05C85 G.2.2
no code implementations • 12 Jan 2021 • Fedor V. Fomin, Petr A. Golovach, Fahad Panolan, Geevarghese Philip, Saket Saurabh
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
no code implementations • 29 Dec 2020 • Fedor V. Fomin, Pierre Fraigniaud, Petr A. Golovach
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.
no code implementations • 19 Oct 2020 • Eduard Eiben, Fedor V. Fomin, Petr A. Golovach, William Lochet, Fahad Panolan, Kirill Simonov
We consider a generalization of the fundamental $k$-means clustering for data with incomplete or corrupted entries.
no code implementations • 20 Jul 2020 • Sayan Bandyapadhyay, Fedor V. Fomin, Kirill Simonov
The new construction allows us to obtain the first coreset for fair clustering in general metric spaces.
no code implementations • 10 May 2019 • Fedor V. Fomin, Petr A. Golovach, Fahad Panolan, Kirill Simonov
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.
no code implementations • 18 Jul 2018 • Fedor V. Fomin, Petr A. Golovach, Daniel Lokshtanov, Fahad Panolan, Saket Saurabh
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.