AIMC Topic: Empirical Research

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Non-Gaussian Methods for Causal Structure Learning.

Prevention science : the official journal of the Society for Prevention Research Apr 1, 2019
Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Neverth...
Machine Learning Causality Empirical Research
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How AI technology can tame the scientific literature.

Nature Sep 1, 2018
Artificial Intelligence Reproducibility of Results Models, Theoretical Research Report Biological Ontologies Search Engine Empirical Research
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Can We Train Machine Learning Methods to Outperform the High-dimensional Propensity Score Algorithm?

Epidemiology (Cambridge, Mass.) Mar 1, 2018
The use of retrospective health care claims datasets is frequently criticized for the lack of complete information on potential confounders. Utilizing patient's health status-related information from claims datasets as surrogates or proxies for misme...
Machine Learning Algorithms Retrospective Studies Data Accuracy Datasets as Topic United Kingdom Propensity Score Empirical Research
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Reinforcement learning improves behaviour from evaluative feedback.

Nature May 28, 2015
Reinforcement learning is a branch of machine learning concerned with using experience gained through interacting with the world and evaluative feedback to improve a system's ability to make behavioural decisions. It has been called the artificial in...
Artificial Intelligence Algorithms Feedback Time Factors Reward Markov Chains Monte Carlo Method Empirical Research
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