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Alessandro Magnani
Researcher at Walmart Labs
Publications - 34
Citations - 1099
Alessandro Magnani is an academic researcher from Walmart Labs. The author has contributed to research in topics: Computer science & Graph (abstract data type). The author has an hindex of 11, co-authored 28 publications receiving 984 citations. Previous affiliations of Alessandro Magnani include Stanford University & Imperial College London.
Papers
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Journal ArticleDOI
Convex piecewise-linear fitting
Alessandro Magnani,Stephen Boyd +1 more
TL;DR: The method described, which is a variation on the K-means algorithm for clustering, seems to work well in practice, at least on data that can be fit well by a convex function.
Proceedings ArticleDOI
Optimal kernel selection in Kernel Fisher discriminant analysis
TL;DR: This paper considers the problem of finding the optimal kernel, over a given convex set of kernels, and shows that this optimal kernel selection problem can be reformulated as a tractable convex optimization problem which interior-point methods can solve globally and efficiently.
Journal ArticleDOI
Robust Beamforming via Worst-Case SINR Maximization
TL;DR: It is shown that with a general convex uncertainty model, the worst-case SINR maximization problem can be solved by using convex optimization, and the result allows us to handle more general uncertainty models than prior work using a special form of uncertainty model.
Proceedings Article
Robust Fisher Discriminant Analysis
TL;DR: It is shown that with general convex uncertainty models on the problem data, robust Fisher LDA can be carried out using convex optimization using a certain type of product form uncertainty model at a cost comparable to standard Fisher L DA.
Patent
Mapping Advertiser Intents to Keywords
Daniel Galas,Veeravich Thi Thumasathit,Murthy V. Nukala,Richard Edward Chatwin,Alessandro Magnani,Benjamin David Foster,Alan Coleman,Manish Khettry,Siva Chandrasekar,Nitin Gupta,Srinidhi Ramesh Kondaji +10 more
TL;DR: In this paper, a method includes constructing an intent map for a plurality of products, the intent map comprising intent topics and each intent topic comprising intents, and then deriving the plurality of keywords from the intent maps based on keyword templates.