Exploratory Data Analysis (EDA) is an iterative and often tedious process. Several strategies have been proposed to ease the burden on users in EDA ranging from stepwise to full-guidance approaches. Stepwise approaches rely on computing utility functions that determine the best action to take at each step. Full-guidance approaches rely on learning end-to-end exploration policies. Today’s big question is how to commodify EDA and make it easily deployable for all but for that we need to know what users are looking for: are they looking for a needle in a haystack, taking a tour of the data, or are they feeling lucky? This talk will investigate those questions and discuss the challenges of storing learned pathways through data or regenerating them when needed. [Go to the full record in the library's catalogue]
This video is presented here with the permission of the speakers.
Any downloading, storage, reproduction, and redistribution are strictly prohibited
without the prior permission of the respective speakers.
Go to Full Disclaimer.
This video is archived and disseminated for educational purposes only. It is presented here with the permission of the speakers, who have mandated the means of dissemination.
Statements of fact and opinions expressed are those of the inditextual participants. The HKBU and its Library assume no responsibility for the accuracy, validity, or completeness of the information presented.
Any downloading, storage, reproduction, and redistribution, in part or in whole, are strictly prohibited without the prior permission of the respective speakers. Please strictly observe the copyright law.