11 citations · 17 across the 3 of their papers we have counts for
6 papers · 1 filter
Real-Time Decentralized knowledge Transfer at the Edge
Orpaz Goldstein, Mohammad Kachuee, Derek Shiell +1
The proliferation of edge networks creates islands of learning agents working on local streams of data. Transferring knowledge between these agents in real-time without exposing pr…
Cost-Sensitive Feature-Value Acquisition Using Feature Relevance
Kimmo Kärkkäinen, Mohammad Kachuee, Orpaz Goldstein +1
In many real-world machine learning problems, feature values are not readily available. To make predictions, some of the missing features have to be acquired, which can incur a cos…
Target-Focused Feature Selection Using a Bayesian Approach
Orpaz Goldstein, Mohammad Kachuee, Kimmo Karkkainen +1
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…
Generative Imputation and Stochastic Prediction
Mohammad Kachuee, Kimmo Karkkainen, Orpaz Goldstein +2
In many machine learning applications, we are faced with incomplete datasets. In the literature, missing data imputation techniques have been mostly concerned with filling missing…
Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams
Mohammad Kachuee, Orpaz Goldstein, Kimmo Karkkainen +2
In many real-world learning scenarios, features are only acquirable at a cost constrained under a budget. In this paper, we propose a novel approach for cost-sensitive feature acqu…
Cost-Sensitive Diagnosis and Learning Leveraging Public Health Data
Mohammad Kachuee, Kimmo Karkkainen, Orpaz Goldstein +2
Traditionally, machine learning algorithms rely on the assumption that all features of a given dataset are available for free. However, there are many concerns such as monetary dat…