no code implementations • 6 Oct 2023 • Ananth Mahadevan, Michael Mathioudakis
Our main contribution is a Cost-Aware Retraining Algorithm called Cara, which optimizes the trade-off over streams of data and queries.
no code implementations • 6 Oct 2023 • Sachith Pai, Michael Mathioudakis, Yanhao Wang
Specifically, we first formulate a cost function to measure the performance of a Z-index on a dataset for a range-query workload.
no code implementations • 5 Jan 2023 • Yanhao Wang, Michael Mathioudakis, Jia Li, Francesco Fabbri
Diversity maximization aims to select a diverse and representative subset of items from a large dataset.
1 code implementation • 8 Nov 2022 • Arpit Merchant, Michael Mathioudakis, Yanhao Wang
By initially allowing relaxed (fractional) solutions for integer maximization, we analytically expose the underlying connections to the spectral properties of the adjacency matrix.
1 code implementation • 30 Jul 2022 • Yanhao Wang, Francesco Fabbri, Michael Mathioudakis
Given a set $X$ of $n$ elements, it asks to select a subset $S$ of $k \ll n$ elements with maximum \emph{diversity}, as quantified by the dissimilarities among the elements in $S$.
1 code implementation • 1 Feb 2022 • Francesco Fabbri, Yanhao Wang, Francesco Bonchi, Carlos Castillo, Michael Mathioudakis
Hence, we define the problem of reducing the prevalence of radicalization pathways by selecting a small number of edges to "rewire", so to minimize the maximum of segregation scores among all radicalized nodes, while maintaining the relevance of the recommendations.
1 code implementation • 29 Jun 2021 • Ananth Mahadevan, Michael Mathioudakis
In this paper, we present an experimental study of the three state-of-the-art approximate unlearning methods for linear models and demonstrate the trade-offs between efficiency, effectiveness and certifiability offered by each method.
no code implementations • 22 Oct 2020 • Arpit Merchant, Michael Mathioudakis
The task of node classification is to infer unknown node labels, given the labels for some of the nodes along with the network structure and other node attributes.
1 code implementation • 9 Oct 2020 • Yanhao Wang, Francesco Fabbri, Michael Mathioudakis
We study the problem of extracting a small subset of representative items from a large data stream.
no code implementations • 19 Jul 2020 • Yanhao Wang, Michael Mathioudakis, Yuchen Li, Kian-Lee Tan
Extracting a small subset of representative tuples from a large database is an important task in multi-criteria decision making.
Data Structures and Algorithms Databases
no code implementations • 2 Jul 2020 • Corinna Hertweck, Carlos Castillo, Michael Mathioudakis
In this paper, we study university admissions under a centralized system that uses grades and standardized test scores to match applicants to university programs.
1 code implementation • 5 Jan 2018 • Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, Michael Mathioudakis
By comparing the two, we find that Twitter users are, to a large degree, exposed to political opinions that agree with their own.
Social and Information Networks
1 code implementation • 18 Jul 2015 • Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, Michael Mathioudakis
Unlike previous work, rather than study controversy in a single hand-picked topic and use domain specific knowledge, we take a general approach to study topics in any domain.
Social and Information Networks