Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Galhotra, Sainyam"'
Autor:
Galhotra, Sainyam, Halpern, Joseph Y.
Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significan
Externí odkaz:
http://arxiv.org/abs/2405.14728
Publikováno v:
SIGMOD 2024
The task of extracting a diverse subset from a dataset, often referred to as maximum diversification, plays a pivotal role in various real-world applications that have far-reaching consequences. In this work, we delve into the realm of fairness-aware
Externí odkaz:
http://arxiv.org/abs/2404.04713
Autor:
Han, Minbiao, Light, Jonathan, Xia, Steven, Galhotra, Sainyam, Fernandez, Raul Castro, Xu, Haifeng
Data fuels machine learning (ML) - rich and high-quality training data is essential to the success of ML. However, to transform ML from the race among a few large corporations to an accessible technology that serves numerous normal users' data analys
Externí odkaz:
http://arxiv.org/abs/2310.17843
Data is a central component of machine learning and causal inference tasks. The availability of large amounts of data from sources such as open data repositories, data lakes and data marketplaces creates an opportunity to augment data and boost those
Externí odkaz:
http://arxiv.org/abs/2304.09068
The recent efforts in automation of machine learning or data science has achieved success in various tasks such as hyper-parameter optimization or model selection. However, key areas such as utilizing domain knowledge and data semantics are areas whe
Externí odkaz:
http://arxiv.org/abs/2303.01378
This paper proposes a novel framework for certifying the fairness of predictive models trained on biased data. It draws from query answering for incomplete and inconsistent databases to formulate the problem of consistent range approximation (CRA) of
Externí odkaz:
http://arxiv.org/abs/2212.10839
Publikováno v:
Journal of Machine Learning Research (JMLR) 2023
To capture the inherent geometric features of many community detection problems, we propose to use a new random graph model of communities that we call a Geometric Block Model. The geometric block model builds on the random geometric graphs (Gilbert,
Externí odkaz:
http://arxiv.org/abs/2206.11303
What-if (provisioning for an update to a database) and how-to (how to modify the database to achieve a goal) analyses provide insights to users who wish to examine hypothetical scenarios without making actual changes to a database and thereby help pl
Externí odkaz:
http://arxiv.org/abs/2203.14692
We present Ver, a data discovery system that identifies project-join views over large repositories of tables that do not contain join path information, and even when input queries are inaccurate. Ver implements a reference architecture to solve both
Externí odkaz:
http://arxiv.org/abs/2106.01543
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