Wednesday, October 22, 2025

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2025-10-28 / 16:30 ~ 17:30
IBS-KAIST 세미나 - 이산수학: A Dividing Line for Structural Kernelization of Component Order Connectivity via Distance to Bounded Pathwidth 인쇄
by Jakob Greilhuber(CISPA Helmholtz Center for Information Security)
Vertex Cover is perhaps the most-studied problem in parameterized complexity that frequently serves as a testing ground for new concepts and techniques. In this talk, I will focus on a generalization of Vertex Cover called Component Order Connectivity (COC). Given a graph G, an integer k and a positive integer d, the task is to decide whether there is a vertex set S of size at most k such that each connected component of G – S has size at most d. If d = 1, then COC is the same as Vertex Cover. While almost all techniques to obtain polynomial kernels for Vertex Cover extend well to COC parameterized by k + d, the same cannot be said for structural parameters. Vertex Cover admits a polynomial kernel parameterized by the vertex deletion distance to treewidth 1 graphs, but not when parameterized by the deletion distance to treewidth 2 graphs. The picture changes when considering COC: It was recently shown that COC does not admit a polynomial kernel parameterized by the vertex deletion distance to treewidth 1 graphs with pathwidth 2, even if d ≥ 2 is a fixed constant. Complementing this, we show that COC does admit a polynomial kernel parameterized by the distance to graphs with pathwidth at most 1 (plus d). Hence, the deletion distance to pathwidth 1 vs. pathwidth 2 forms a similar line of tractability for COC as the distance to treewidth 1 vs. treewidth 2 does for Vertex Cover. In this talk, I will highlight the ideas and techniques that make this kernelization result possible.
2025-10-28 / 16:00 ~ 17:00
SAARC 세미나 - SAARC 세미나: 인쇄
by 김일문(KAIST 수리과학과)
In this talk, we will discuss the discrete argmin inference problem in high-dimensional settings. Given n observations from a d dimensional vector, the goal is to test whether the rth component of the mean vector is the smallest among all components. We propose dimension-agnostic tests that maintain validity regardless of how d scales with n, and regardless of arbitrary ties in the mean vector. Notably, our validity holds under mild moment conditions, requiring little more than finiteness of a second moment, and permitting possibly strong dependence between coordinates. In addition, we establish the local minimax separation rate for this problem, which adapts to the cardinality of a confusion set, and show that the proposed tests attain this rate. Empirical results illustrate the strong performance of our approach in terms of type I error control and power compared to existing methods.
2025-10-29 / 16:00 ~ 17:00
IBS-KAIST 세미나 - IBS-KAIST 세미나: 인쇄
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