Individual Fairness for k-Clustering. Sepideh Mahabadi · Ali Vakilian. Tue Jul 14 07:00 AM -- 07:45 AM & Tue Jul 14 06:00 PM -- 06:45 PM (PDT) @ Virtual
Sepideh Mahabadi's 24 research works with 180 citations and 395 reads, including: Streaming Complexity of SVMs
Join Facebook to connect with Sepideh Mahabadi and others you may know. Facebook gives people the Sepideh Mahabadi is a postdoctoral research scientist with an appointment at the Simons Collaboration on Algorithms and Geometry based at Columbia University, working with Alexandr Andoni. She received her Ph.D. in computer science from the Massachusetts Institute of Technology in 2017, under the supervision of Piotr Indyk. Sepideh Mahabadi. Toyota Technological Institute at Chicago. Assistant Professor.
Country, Tasks, Score, Rank, Medal. Abs. Rel. Abs. Rel. 2007 · Iran · 100 · 40 · 100 · 100 · 64 · 5, 409, 68.17% Sepideh Mahabadi. MathSciNet. Ph.D. Massachusetts Institute of Technology 2017 UnitedStates.
Sepideh Mahabadi Toyota Technological Institute at Chicago. Searching and summarization are two of the most fundamental tasks in massive data analysis. In this talk, I will focus on these two tasks from the perspective of diversity and fairness. Search is often formalized as the (approximate) nearest neighbor problem.
Authors. Sariel Har-Peled, Sepideh Mahabadi. Abstract. In this work we study a " fair" variant of the near neighbor problem.
Sepideh Mahabadi is a research assistant professor at the Toyota Technological Institute at Chicago (TTIC). She received her PhD from MIT, where she was advised by Piotr Indyk. For a year, she was a postdoctoral research scientist at Simons Collaboration on Algorithms and Geometry based at Columbia University.
▽. Country, Tasks, Score, Rank, Medal. Abs. Rel. Abs. Rel. 2007 · Iran · 100 · 40 · 100 · 100 · 64 · 5, 409, 68.17% Sepideh Mahabadi.
hassan fallah 9 ай бұрын.
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Ph.D. Massachusetts Institute of Technology 2017 UnitedStates. Dissertation: Sub-linear Algorithms for Massive Data Problems.
Hemadress Sepideh Hatami 35 år.
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Sepideh Mahabadi received 2011 her B.Sc. in Computer Engineering from the Sharif University of Technology, Iran. That year she moved to the United States to
For a year, she was a postdoctoral research scientist at Simons Collaboration on Algorithms and Geometry based at Columbia University. Sepideh Mahabadi Toyota Technological Institute at Chicago. Searching and summarization are two of the most fundamental tasks in massive data analysis. In this talk, I will focus on these two tasks from the perspective of diversity and fairness. Search is often formalized as the (approximate) nearest neighbor problem.