Search Results for author:
Found 6 papers, 4 papers with code
Real-Time Decentralized knowledge Transfer at the Edge
Transferring knowledge in a selective decentralized approach enables models to retain their local insights, allowing for local flavors of a machine learning model.
Cost-Sensitive Feature-Value Acquisition Using Feature Relevance
The chosen features should increase the prediction accuracy for a low cost, but determining which features will do that is challenging.
Target-Focused Feature Selection Using a Bayesian Approach
In many real-world scenarios where data is high dimensional, test time acquisition of features is a non-trivial task due to costs associated with feature acquisition and evaluating feature value.
Generative Imputation and Stochastic Prediction
In order to make imputations, we train a simple and effective generator network to generate imputations that a discriminator network is tasked to distinguish.
Cost-Sensitive Diagnosis and Learning Leveraging Public Health Data
Furthermore, based on the suggested dataset, we provide a comparison of recent and state-of-the-art approaches to cost-sensitive feature acquisition and learning.
Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams
The suggested method acquires features incrementally based on a context-aware feature-value function.
