Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Meliou, Alexandra"'
Data-driven decision-making is at the core of many modern applications, and understanding the data is critical in supporting trust in these decisions. However, data is dynamic and evolving, just like the real-world entities it represents. Thus, an im
Externí odkaz:
http://arxiv.org/abs/2409.18386
Autor:
Mai, Anh L., Wang, Pengyu, Abouzied, Azza, Brucato, Matteo, Haas, Peter J., Meliou, Alexandra
A package query returns a package - a multiset of tuples - that maximizes or minimizes a linear objective function subject to linear constraints, thereby enabling in-database decision support. Prior work has established the equivalence of package que
Externí odkaz:
http://arxiv.org/abs/2307.02860
Autor:
Yang, Ke, Meliou, Alexandra
Machine Learning (ML) models are widely employed to drive many modern data systems. While they are undeniably powerful tools, ML models often demonstrate imbalanced performance and unfair behaviors. The root of this problem often lies in the fact tha
Externí odkaz:
http://arxiv.org/abs/2303.17566
Given an $n$-point metric space $(\mathcal{X},d)$ where each point belongs to one of $m=O(1)$ different categories or groups and a set of integers $k_1, \ldots, k_m$, the fair Max-Min diversification problem is to select $k_i$ points belonging to cat
Externí odkaz:
http://arxiv.org/abs/2201.06678
Autor:
Galhotra, Sainyam, Fariha, Anna, Lourenço, Raoni, Freire, Juliana, Meliou, Alexandra, Srivastava, Divesh
As data is a central component of many modern systems, the cause of a system malfunction may reside in the data, and, specifically, particular properties of the data. For example, a health-monitoring system that is designed under the assumption that
Externí odkaz:
http://arxiv.org/abs/2105.06058
Publikováno v:
SIGMOD 2020
We provide methods for in-database support of decision making under uncertainty. Many important decision problems correspond to selecting a package (bag of tuples in a relational database) that jointly satisfy a set of constraints while minimizing so
Externí odkaz:
http://arxiv.org/abs/2103.06784
Classification, a heavily-studied data-driven machine learning task, drives an increasing number of prediction systems involving critical human decisions such as loan approval and criminal risk assessment. However, classifiers often demonstrate discr
Externí odkaz:
http://arxiv.org/abs/2101.07361
Traditional data systems require specialized technical skills where users need to understand the data organization and write precise queries to access data. Therefore, novice users who lack technical expertise face hurdles in perusing and analyzing d
Externí odkaz:
http://arxiv.org/abs/2012.14800
Diversity is an important principle in data selection and summarization, facility location, and recommendation systems. Our work focuses on maximizing diversity in data selection, while offering fairness guarantees. In particular, we offer the first
Externí odkaz:
http://arxiv.org/abs/2010.09141
Runtime nondeterminism is a fact of life in modern database applications. Previous research has shown that nondeterminism can cause applications to intermittently crash, become unresponsive, or experience data corruption. We propose Adaptive Interven
Externí odkaz:
http://arxiv.org/abs/2003.09539