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Lirio Research: A Novel Policy Comparison Metric for Reinforcement Learning

Lirio Research: A Novel Policy Comparison Metric for Reinforcement Learning

Christopher Symons, Ph.D. | Sep 3, 2021 | Artificial Intelligence, Behavioral Science, Blog

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.

Lirio Recognized as Best AI Startup at AI Summit’s AIconics Awards

Lirio Recognized as Best AI Startup at AI Summit’s AIconics Awards

Patrick Hunt | Dec 10, 2020 | Artificial Intelligence, News

Lirio is both proud and honored to have been named Best AI Startup at the 2020 AIconics Awards ceremony at the AI Summit New York. Lirio was also recognized as “Highly Commended” for Best Innovation in Deep Learning. AI Summit is the world’s leading event series...
Optimizing for Change: Why Your AI Isn’t Solving Your Problem

Optimizing for Change: Why Your AI Isn’t Solving Your Problem

Golden Spiral | Oct 14, 2020 |

Optimizing for Change: Why Your AI Isn’t Solving Your Problem Featuring Lirio’s Chris Symons, Ph.D., Chief Artificial Intelligence Scientist, & Clayton Webster, Ph.D., Distinguished Research Fellow The potential to democratize healthcare through Artificial...
Lirio Precision Nudging™: The Solution to Scalable Behavior Change

Lirio Precision Nudging™: The Solution to Scalable Behavior Change

Golden Spiral | Sep 10, 2020 |

Lirio Precision Nudging™: The Solution to Scalable Behavior Change Featuring Chandra Osborn, Ph.D., Susanne Blazek, Ph.D., Nicholas Griffin, Ph.D. Transformative digital health is well-anticipated but falls short in delivering scalable behavior change. The key to...
Lirio Research: Introducing A New Form of Machine Learning Optimization

Lirio Research: Introducing A New Form of Machine Learning Optimization

Christopher Symons, Ph.D. and Clayton Webster, Ph.D. | Jul 1, 2020 | Artificial Intelligence, Blog, Digital Health

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 is simple to implement and does not...
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