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pro vyhledávání: '"Black William"'
Autor:
Black, William, Manlove, Alexander, Pennington, Jack, Marchini, Andrea, Ilhan, Ercument, Markeviciute, Vilda
For users navigating travel e-commerce websites, the process of researching products and making a purchase often results in intricate browsing patterns that span numerous sessions over an extended period of time. The resulting clickstream data chroni
Externí odkaz:
http://arxiv.org/abs/2409.12972
Autor:
Fu, Shenming, Dell'Antonio, Ian, Escalante, Zacharias, Nelson, Jessica, Englert, Anthony, Helhoski, Søren, Shinde, Rahul, Brockland, Julia, LaDuca, Philip, Larkin, Christelyn, Paris, Lucca, Weiner, Shane, Black, William K., Chary, Ranga-Ram, Clowe, Douglas, Cooper, M. C., Donahue, Megan, Evrard, August, Lacy, Mark, Lauer, Tod, Liu, Binyang, McCleary, Jacqueline, Meneghetti, Massimo, Miyatake, Hironao, Montes, Mireia, Natarajan, Priyamvada, Ntampaka, Michelle, Pierpaoli, Elena, Postman, Marc, Sohn, Jubee, Turner, David, Umetsu, Keiichi, Utsumi, Yousuke, Wilson, Gillian
The Local Volume Complete Cluster Survey (LoVoCCS) is an on-going program to observe nearly a hundred low-redshift X-ray-luminous galaxy clusters (redshifts $0.0310^{44}$ erg/s)
Externí odkaz:
http://arxiv.org/abs/2402.10337
Autor:
Black, William K., Evrard, August E.
Publikováno v:
The Open Journal of Astrophysics 7, 116171 (2024)
Using the photometric population prediction method {\bf Red Dragon}, we characterize the Red Sequence (RS) and Blue Cloud (BC) of DES galaxies in the COSMOS field. Red Dragon (RD) uses a redshift-evolving, error-corrected Gaussian mixture model to de
Externí odkaz:
http://arxiv.org/abs/2310.09374
Publikováno v:
Proceedings of the 17th ACM Conference on Recommender Systems, 2023, pp. 426-429
This paper presents AdaptEx, a self-service contextual bandit platform widely used at Expedia Group, that leverages multi-armed bandit algorithms to personalize user experiences at scale. AdaptEx considers the unique context of each visitor to select
Externí odkaz:
http://arxiv.org/abs/2308.08650
Problem Roulette (PR), an online study service at the University of Michigan, offers points-free formative practice to students preparing for examinations in introductory STEM courses. Using four years of PR data involving millions of problem attempt
Externí odkaz:
http://arxiv.org/abs/2301.02927