Zobrazeno 1 - 10
of 72
pro vyhledávání: '"Karimireddy, Sai Praneeth"'
We study collaborative learning systems in which the participants are competitors who will defect from the system if they lose revenue by collaborating. As such, we frame the system as a duopoly of competitive firms who are each engaged in training m
Externí odkaz:
http://arxiv.org/abs/2406.15898
Collaboration between different data centers is often challenged by heterogeneity across sites. To account for the heterogeneity, the state-of-the-art method is to re-weight the covariate distributions in each site to match the distribution of the ta
Externí odkaz:
http://arxiv.org/abs/2404.15746
Autor:
Ananthakrishnan, Nivasini, Ding, Tiffany, Werner, Mariel, Karimireddy, Sai Praneeth, Jordan, Michael I.
We study price-discrimination games between buyers and a seller where privacy arises endogenously--that is, utility maximization yields equilibrium strategies where privacy occurs naturally. In this game, buyers with a high valuation for a good have
Externí odkaz:
http://arxiv.org/abs/2404.10767
Autor:
Lu, Charles, Huang, Baihe, Karimireddy, Sai Praneeth, Vepakomma, Praneeth, Jordan, Michael, Raskar, Ramesh
Acquiring high-quality training data is essential for current machine learning models. Data markets provide a way to increase the supply of data, particularly in data-scarce domains such as healthcare, by incentivizing potential data sellers to join
Externí odkaz:
http://arxiv.org/abs/2403.13893
We present Scaff-PD, a fast and communication-efficient algorithm for distributionally robust federated learning. Our approach improves fairness by optimizing a family of distributionally robust objectives tailored to heterogeneous clients. We levera
Externí odkaz:
http://arxiv.org/abs/2307.13381
Identifying clients with similar objectives and learning a model-per-cluster is an intuitive and interpretable approach to personalization in federated learning. However, doing so with provable and optimal guarantees has remained an open challenge. W
Externí odkaz:
http://arxiv.org/abs/2306.08393
For a federated learning model to perform well, it is crucial to have a diverse and representative dataset. However, the data contributors may only be concerned with the performance on a specific subset of the population, which may not reflect the di
Externí odkaz:
http://arxiv.org/abs/2306.05592
Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty quantification in machine learning since it can be easily applied as a post-processing step to already trained models. In this paper, we extend conformal predic
Externí odkaz:
http://arxiv.org/abs/2305.17564
The creator economy has revolutionized the way individuals can profit through online platforms. In this paper, we initiate the study of online learning in the creator economy by modeling the creator economy as a three-party game between the users, pl
Externí odkaz:
http://arxiv.org/abs/2305.11381
Ensuring fairness is a crucial aspect of Federated Learning (FL), which enables the model to perform consistently across all clients. However, designing an FL algorithm that simultaneously improves global model performance and promotes fairness remai
Externí odkaz:
http://arxiv.org/abs/2301.12407