10 citations · 17 across the 3 of their papers we have counts for
9 papers
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings
Elliot Meyerson, Risto Miikkulainen
This paper frames a general prediction system as an observer traveling around a continuous space, measuring values at some locations, and predicting them at others. The observer is…
From Prediction to Prescription: Evolutionary Optimization of Non-Pharmaceutical Interventions in the COVID-19 Pandemic
Risto Miikkulainen, Olivier Francon, Elliot Meyerson +3
Several models have been developed to predict how the COVID-19 pandemic spreads, and how it could be contained with non-pharmaceutical interventions (NPIs) such as social distancin…
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted Prescription
Olivier Francon, Santiago Gonzalez, Babak Hodjat +4
There is now significant historical data available on decision making in organizations, consisting of the decision problem, what decisions were made, and how desirable the outcomes…
Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel
Xin Qiu, Elliot Meyerson, Risto Miikkulainen
Neural Networks (NNs) have been extensively used for a wide spectrum of real-world regression tasks, where the goal is to predict a numerical outcome such as revenue, effectiveness…
Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains
Elliot Meyerson, Risto Miikkulainen
As deep learning applications continue to become more diverse, an interesting question arises: Can general problem solving arise from jointly learning several such diverse tasks? T…
Evolutionary Neural AutoML for Deep Learning
Jason Liang, Elliot Meyerson, Babak Hodjat +3
Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem domains. However, the success of DNNs depends on the proper configuration of its a…