Lirio’s Behavioral Reinforcement Learning Lab (BReLL) recently published a paper describing a new approach, the Limited Data Estimator, for comparing reinforcement learning policies using limited historical data.
Articles by Chris_Symons
Machine Learning Can Help Illuminate Bias in Health Care
There have long been concerns around bias in machine learning (ML) models that interact with or impact people. The recent firing of Timnit Gebru, a prominent AI ethics researcher at […]
Lirio Research: Introducing A New Form of Machine Learning Optimization
Lirio’s AI Research team recently developed a novel adaptive stochastic gradient-free (ASGF) approach for solving some of the most difficult optimization challenges in machine learning. This innovative optimization algorithm, which […]
What the BReLL Means for the Future of Behavior Change AI
Recently, we were excited to announce the formation of Lirio’s Behavioral Reinforcement Learning Lab (BReLL), and the addition of Dr. John Seely Brown (JSB) as the Lab’s scientific advisor. We view […]