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pro vyhledávání: '"Makhija, Disha"'
Achieving fairness across diverse clients in Federated Learning (FL) remains a significant challenge due to the heterogeneity of the data and the inaccessibility of sensitive attributes from clients' private datasets. This study addresses this issue
Externí odkaz:
http://arxiv.org/abs/2406.17102
Machine learning methods for estimating heterogeneous treatment effects (HTE) facilitate large-scale personalized decision-making across various domains such as healthcare, policy making, education, and more. Current machine learning approaches for H
Externí odkaz:
http://arxiv.org/abs/2402.17705
In several practical applications of federated learning (FL), the clients are highly heterogeneous in terms of both their data and compute resources, and therefore enforcing the same model architecture for each client is very limiting. Moreover, the
Externí odkaz:
http://arxiv.org/abs/2306.07959
Federated Learning has become an important learning paradigm due to its privacy and computational benefits. As the field advances, two key challenges that still remain to be addressed are: (1) system heterogeneity - variability in the compute and/or
Externí odkaz:
http://arxiv.org/abs/2205.12493
With growing concerns regarding data privacy and rapid increase in data volume, Federated Learning(FL) has become an important learning paradigm. However, jointly learning a deep neural network model in a FL setting proves to be a non-trivial task be
Externí odkaz:
http://arxiv.org/abs/2202.07757
Autor:
S, Shreyas, Maheshwari, Harsh, Saha, Avijit, Datta, Samik, Jain, Shashank, Makhija, Disha, Nagpal, Anuj, Shukla, Sneha, S, Suyash
Consumable categories, such as grocery and fast-moving consumer goods, are quintessential to the growth of e-commerce marketplaces in developing countries. In this work, we present the design and implementation of a precision merchandising system, wh
Externí odkaz:
http://arxiv.org/abs/2011.08575
Autor:
Kumar, Srijan, Hooi, Bryan, Makhija, Disha, Kumar, Mohit, Faloutsos, Christos, Subrahamanian, V. S.
Rating platforms enable large-scale collection of user opinion about items (products, other users, etc.). However, many untrustworthy users give fraudulent ratings for excessive monetary gains. In the paper, we present FairJudge, a system to identify
Externí odkaz:
http://arxiv.org/abs/1703.10545
Autor:
Hooi, Bryan, Shah, Neil, Beutel, Alex, Gunnemann, Stephan, Akoglu, Leman, Kumar, Mohit, Makhija, Disha, Faloutsos, Christos
Review fraud is a pervasive problem in online commerce, in which fraudulent sellers write or purchase fake reviews to manipulate perception of their products and services. Fake reviews are often detected based on several signs, including 1) they occu
Externí odkaz:
http://arxiv.org/abs/1511.06030
Autor:
Shah, Neil, Beutel, Alex, Hooi, Bryan, Akoglu, Leman, Gunnemann, Stephan, Makhija, Disha, Kumar, Mohit, Faloutsos, Christos
Given a network with attributed edges, how can we identify anomalous behavior? Networks with edge attributes are commonplace in the real world. For example, edges in e-commerce networks often indicate how users rated products and services in terms of
Externí odkaz:
http://arxiv.org/abs/1510.05544
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