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Naamad, Yonatan
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1

Fast and effective distribution-key recommendation for amaz..:

Parchas, Panos ; Naamad, Yonatan ; Van Bouwel, Peter..
Proceedings of the VLDB Endowment.  13 (2020)  12 - p. 2411-2423 , 2020
Link: https://dl.acm.org/doi/1..
 
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2

Fast and effective distribution-key recommendation for amaz..:

Parchas, Panos ; Naamad, Yonatan ; Van Bouwel, Peter..
Proceedings of the VLDB Endowment.  13 (2020)  12 - p. 2411-2423 , 2020
Link: https://doi.org/10.14778..
 
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3

Multi-commodity Flow with In-Network Processing:

, In: Algorithmic Aspects of Cloud Computing; Lecture Notes in Computer Science,
Charikar, Moses ; Naamad, Yonatan ; Rexford, Jenifer. - p. 73-101 , 2019
Link: https://doi.org/10.1007/..
 
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4

Intervention-Aware Early Warning:

, In: 2019 IEEE International Conference on Data Mining (ICDM),
Eswaran, Dhivya ; Faloutsos, Christos ; Mishra, Nina. - p. 1030-1035 , 2019
Link: https://doi.org/10.1109/..
 
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5

Anarchy, stability, and utopia: creating better matchings:

Anshelevich, Elliot ; Das, Sanmay ; Naamad, Yonatan
Autonomous Agents and Multi-Agent Systems.  26 (2011)  1 - p. 120-140 , 2011
Link: https://doi.org/10.1007/..
 
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6

Fast Private Kernel Density Estimation via Locality Sensiti..:

Wagner, Tal ; Naamad, Yonatan ; Mishra, Nina
http://arxiv.org/abs/2307.01877.  , 2023
Link: http://arxiv.org/abs/230..
 
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7

Explaining a machine learning decision to physicians via co..:

Nagesh, Supriya ; Mishra, Nina ; Naamad, Yonatan...
http://arxiv.org/abs/2306.06325.  , 2023
Link: http://arxiv.org/abs/230..
 
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8

On Finding Dense Common Subgraphs:

Charikar, Moses ; Naamad, Yonatan ; Wu, Jimmy
http://arxiv.org/abs/1802.06361.  , 2018
Link: http://arxiv.org/abs/180..
 
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9

Multi-Commodity Flow with In-Network Processing:

Charikar, Moses ; Naamad, Yonatan ; Rexford, Jennifer.
http://arxiv.org/abs/1802.09118.  , 2018
Link: http://arxiv.org/abs/180..
 
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10

On Approximating Target Set Selection:

Charikar, Moses ; Naamad, Yonatan ; Wirth, Anthony
Is Part Of LIPIcs, Volume 60, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2016).  , 2016
Link: https://doi.org/10.4230/..
 
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