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pro vyhledávání: '"Khurana, Udayan"'
Autor:
Zhu, Qian, Wang, Dakuo, Ma, Shuai, Wang, April Yi, Chen, Zixin, Khurana, Udayan, Ma, Xiaojuan
As AI technology continues to advance, the importance of human-AI collaboration becomes increasingly evident, with numerous studies exploring its potential in various fields. One vital field is data science, including feature engineering (FE), where
Externí odkaz:
http://arxiv.org/abs/2405.14107
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
Data Scientists leverage common sense reasoning and domain knowledge to understand and enrich data for building predictive models. In recent years, we have witnessed a surge in tools and techniques for {\em automated machine learning}. While data sci
Externí odkaz:
http://arxiv.org/abs/2205.08018
Autor:
Wang, Dakuo, Liao, Q. Vera, Zhang, Yunfeng, Khurana, Udayan, Samulowitz, Horst, Park, Soya, Muller, Michael, Amini, Lisa
Data science and machine learning (DS/ML) are at the heart of the recent advancements of many Artificial Intelligence (AI) applications. There is an active research thread in AI, \autoai, that aims to develop systems for automating end-to-end the DS/
Externí odkaz:
http://arxiv.org/abs/2101.03970
Autor:
Khurana, Udayan, Galhotra, Sainyam
Detecting semantic concept of columns in tabular data is of particular interest to many applications ranging from data integration, cleaning, search to feature engineering and model building in machine learning. Recently, several works have proposed
Externí odkaz:
http://arxiv.org/abs/2012.08594
Autor:
Aggarwal, Charu, Bouneffouf, Djallel, Samulowitz, Horst, Buesser, Beat, Hoang, Thanh, Khurana, Udayan, Liu, Sijia, Pedapati, Tejaswini, Ram, Parikshit, Rawat, Ambrish, Wistuba, Martin, Gray, Alexander
Data science is labor-intensive and human experts are scarce but heavily involved in every aspect of it. This makes data science time consuming and restricted to experts with the resulting quality heavily dependent on their experience and skills. To
Externí odkaz:
http://arxiv.org/abs/1910.14436
Autor:
Khurana, Udayan, Samulowitz, Horst
Building a good predictive model requires an array of activities such as data imputation, feature transformations, estimator selection, hyper-parameter search and ensemble construction. Given the large, complex and heterogenous space of options, off-
Externí odkaz:
http://arxiv.org/abs/1903.00743
Feature engineering is a crucial step in the process of predictive modeling. It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target. However,
Externí odkaz:
http://arxiv.org/abs/1709.07150
Autor:
Khurana, Udayan, Deshpande, Amol
The work on large-scale graph analytics to date has largely focused on the study of static properties of graph snapshots. However, a static view of interactions between entities is often an oversimplification of several complex phenomena like the spr
Externí odkaz:
http://arxiv.org/abs/1509.08960
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