Zobrazeno 1 - 10
of 129
pro vyhledávání: '"Assefa, Samuel"'
Federated learning holds great promise in learning from fragmented sensitive data and has revolutionized how machine learning models are trained. This article provides a systematic overview and detailed taxonomy of federated learning. We investigate
Externí odkaz:
http://arxiv.org/abs/2204.13697
Autor:
Lavin, Alexander, Krakauer, David, Zenil, Hector, Gottschlich, Justin, Mattson, Tim, Brehmer, Johann, Anandkumar, Anima, Choudry, Sanjay, Rocki, Kamil, Baydin, Atılım Güneş, Prunkl, Carina, Paige, Brooks, Isayev, Olexandr, Peterson, Erik, McMahon, Peter L., Macke, Jakob, Cranmer, Kyle, Zhang, Jiaxin, Wainwright, Haruko, Hanuka, Adi, Veloso, Manuela, Assefa, Samuel, Zheng, Stephan, Pfeffer, Avi
The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Int
Externí odkaz:
http://arxiv.org/abs/2112.03235
Institutions are increasingly relying on machine learning models to identify and alert on abnormal events, such as fraud, cyber attacks and system failures. These alerts often need to be manually investigated by specialists. Given the operational cos
Externí odkaz:
http://arxiv.org/abs/2110.02403
Publikováno v:
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence Main Track. Pages 4214-4220. 2021
We consider a problem wherein jobs arrive at random times and assume random values. Upon each job arrival, the decision-maker must decide immediately whether or not to accept the job and gain the value on offer as a reward, with the constraint that t
Externí odkaz:
http://arxiv.org/abs/2106.04944
Publikováno v:
In Heliyon 30 April 2024 10(8)
Autor:
Aviv, Aviram, Oshrat, Yaniv, Assefa, Samuel A., Mustapha, Tobi, Borrajo, Daniel, Veloso, Manuela, Kraus, Sarit
Call centers, in which human operators attend clients using textual chat, are very common in modern e-commerce. Training enough skilled operators who are able to provide good service is a challenge. We suggest an algorithm and a method to train and i
Externí odkaz:
http://arxiv.org/abs/2105.03986
The ability to generate high-fidelity synthetic data is crucial when available (real) data is limited or where privacy and data protection standards allow only for limited use of the given data, e.g., in medical and financial data-sets. Current state
Externí odkaz:
http://arxiv.org/abs/2101.00598
Publikováno v:
In Land Use Policy February 2024 137
Autor:
Lockhart, Joshua, Assefa, Samuel, Alajdad, Ayham, Alexander, Andrew, Balch, Tucker, Veloso, Manuela
Supervised learning classifiers inevitably make mistakes in production, perhaps mis-labeling an email, or flagging an otherwise routine transaction as fraudulent. It is vital that the end users of such a system are provided with a means of relabeling
Externí odkaz:
http://arxiv.org/abs/2010.05852
Document classification is ubiquitous in a business setting, but often the end users of a classifier are engaged in an ongoing feedback-retrain loop with the team that maintain it. We consider this feedback-retrain loop from a multi-agent point of vi
Externí odkaz:
http://arxiv.org/abs/2004.13152