15 citations · 23 across the 6 of their papers we have counts for
6 papers
A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows
Line Pouchard, Kristofer G. Reyes, Francis J. Alexander +1
The ability to replicate predictions by machine learning (ML) or artificial intelligence (AI) models and results in scientific workflows that incorporate such ML/AI predictions is…
Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels
Marcus M. Noack, Harinarayan Krishnan, Mark D. Risser +1
A Gaussian Process (GP) is a prominent mathematical framework for stochastic function approximation in science and engineering applications. This success is largely attributed to t…
Decision-Making Under Uncertainty for Multi-stage Pipelines: Simulation Studies to Benchmark Screening Strategies
Kristofer G. Reyes, Jiaqian Liu, Carlos Juan Díaz Vargas
Multi-stage screening pipelines are ubiquitous throughout experimental and computational science. Much of the effort in developing screening pipelines focuses on improving generati…
Problem-fluent models for complex decision-making in autonomous materials research
Soojung Baek, Kristofer G. Reyes
We review our recent work in the area of autonomous materials research, highlighting the coupling of machine learning methods and models and more problem-aware modeling. We review…
Optimal Learning for Sequential Decisions in Laboratory Experimentation
Kristopher Reyes, Warren B Powell
The process of discovery in the physical, biological and medical sciences can be painstakingly slow. Most experiments fail, and the time from initiation of research until a new adv…
A Knowledge Gradient Policy for Sequencing Experiments to Identify the Structure of RNA Molecules Using a Sparse Additive Belief Model
Yan Li, Kristofer G. Reyes, Jorge Vazquez-Anderson +3
We present a sparse knowledge gradient (SpKG) algorithm for adaptively selecting the targeted regions within a large RNA molecule to identify which regions are most amenable to int…