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Found 7 papers, 0 papers with code
Federated Learning for Estimating Heterogeneous Treatment Effects
To overcome this obstacle, in this work, we propose a novel framework for collaborative learning of HTE estimators across institutions via Federated Learning.
Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings
Moreover, the need for uncertainty quantification and data privacy constraints are often particularly amplified for clients that have limited local data.
Federated Self-supervised Learning for Heterogeneous Clients
As the field advances, two key challenges that still remain to be addressed are: (1) system heterogeneity - variability in the compute and/or data resources present on each client, and (2) lack of labeled data in certain federated settings.
Architecture Agnostic Federated Learning for Neural Networks
With growing concerns regarding data privacy and rapid increase in data volume, Federated Learning(FL) has become an important learning paradigm.
Audience Creation for Consumables -- Simple and Scalable Precision Merchandising for a Growing Marketplace
Consumable categories, such as grocery and fast-moving consumer goods, are quintessential to the growth of e-commerce marketplaces in developing countries.
FairJudge: Trustworthy User Prediction in Rating Platforms
We propose three metrics: (i) the fairness of a user that quantifies how trustworthy the user is in rating the products, (ii) the reliability of a rating that measures how reliable the rating is, and (iii) the goodness of a product that measures the quality of the product.
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection
To combine these 2 approaches, we formulate our Bayesian Inference for Rating Data (BIRD) model, a flexible Bayesian model of user rating behavior.
