Marcus M. Noack

1. Autonomous Materials Discovery Driven by Gaussian Process Regression\n with Inhomogeneous Measurement Noise and Anisotropic Kernels
   2020/06/03 by Marcus M. Noack, Noack, Marcus M., Gregory S. Doerk +11 · 3 citations
   Biochemistry, Genetics and Molecular Biology · Materials Science · #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (stat.ML) #Machine Learning in Materials Science #Molecular Biology Techniques and Applications #Spectroscopy Techniques in Biomedical and Chemical Research
2. Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels
   2022/05/18 by Marcus M. Noack, Noack, Marcus M., Harinarayan Krishnan +5 · 2 citations
   Computer Science · #Advanced Multi-Objective Optimization Algorithms #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Probability (math.PR)
3. A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes
   2023/09/18 by Marcus M. Noack, Noack, Marcus M., Hengrui Luo +3 · 2 citations
   Computer Science · Engineering · Environmental Science · #Air Quality Monitoring and Forecasting #FOS: Computer and information sciences #FOS: Mathematics #Fault Detection and Control Systems #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Probability (math.PR)
4. Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data
   2024/11/07 by Mark D. Risser, Marcus M. Noack, Risser, Mark D. +5 · 1 citation
   Computer Science · #Applications (stat.AP) #Computation (stat.CO) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Neural Networks and Applications