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05/25/2023
Sequential Bayesian experimental design for calibration of expensive simulation models
Simulation models of critical systems often have parameters that need to...
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04/19/2023
Constructing a simulation surrogate with partially observed output
Gaussian process surrogates are a popular alternative to directly using ...
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04/15/2023
A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps
In order to deploy machine learning in a real-world self-driving laborat...
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04/14/2023
Designing a Framework for Solving Multiobjective Simulation Optimization Problems
Multiobjective simulation optimization (MOSO) problems are optimization ...
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12/30/2022
Hashing-like Johnson–Lindenstrauss transforms and their extreme singular values
The Johnson–Lindenstrauss (JL) lemma is a powerful tool for dimensionali...
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11/29/2022
Numerical evidence against advantage with quantum fidelity kernels on classical data
Quantum machine learning techniques are commonly considered one of the m...
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06/14/2022
Bandwidth Enables Generalization in Quantum Kernel Models
Quantum computers are known to provide speedups over classical state-of-...
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12/28/2021
DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification
Data processing and analysis pipelines in cosmological survey experiment...
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11/09/2021
Importance of Kernel Bandwidth in Quantum Machine Learning
Quantum kernel methods are considered a promising avenue for applying qu...
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09/24/2021
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization
We consider unconstrained stochastic optimization problems with no avail...
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05/20/2021
Lookahead Acquisition Functions for Finite-Horizon Time-Dependent Bayesian Optimization and Application to Quantum Optimal Control
We propose a novel Bayesian method to solve the maximization of a time-d...
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04/19/2021
Randomized Algorithms for Scientific Computing (RASC)
Randomized algorithms have propelled advances in artificial intelligence...
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04/16/2021
libEnsemble: A Library to Coordinate the Concurrent Evaluation of Dynamic Ensembles of Calculations
Almost all applications stop scaling at some point; those that don't are...
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03/30/2021
Scalable Statistical Inference of Photometric Redshift via Data Subsampling
Handling big data has largely been a major bottleneck in traditional sta...
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01/03/2020
Tuning Multigrid Methods with Robust Optimization
Local Fourier analysis is a useful tool for predicting and analyzing the...
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10/29/2019
Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization
We consider stochastic zero-order optimization problems, which arise in ...
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07/26/2019
Sequential Learning of Active Subspaces
In recent years, active subspace methods (ASMs) have become a popular me...
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10/09/2016
Doing Moore with Less -- Leapfrogging Moore's Law with Inexactness for Supercomputing
Energy and power consumption are major limitations to continued scaling ...
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05/31/2016