The need for interpretable features: Motivation and taxonomy
Through extensive experience developing and explaining machine learning (ML)
applications for real-world domains, we have learned that ML models are only as …
applications for real-world domains, we have learned that ML models are only as …
Semantic-Guided RL for Interpretable Feature Engineering
The quality of Machine Learning (ML) models strongly depends on the input data, as such
generating high-quality features is often required to improve the predictive accuracy. This …
generating high-quality features is often required to improve the predictive accuracy. This …
Influence Maximization for Social Good: Use of Social Networks in Low Resource Communities
A Yadav - arXiv preprint arXiv:1912.02105, 2019 - arxiv.org
This thesis proposal makes the following technical contributions:(i) we provide a definition of
the Dynamic Influence Maximization Under Uncertainty (or DIME) problem, which models …
the Dynamic Influence Maximization Under Uncertainty (or DIME) problem, which models …
Artificial Intelligence for Low-Resource Communities: Influence Maximization in an Uncertain World
A Yadav - 2018 - search.proquest.com
Abstract The potential of Artificial Intelligence (AI) to tackle challenging problems that afflict
society is enormous, particularly in the areas of healthcare, conservation and public safety …
society is enormous, particularly in the areas of healthcare, conservation and public safety …