39 citations · 55 across the 10 of their papers we have counts for
11 papers
Trajectory-Oriented Optimization Via Adaptive Thompson Sampling And Grid Refinement: A Tutorial With The ADAPTIVE\_TS Package
David O'Gara, Arindam Fadikar, Mickaël Binois +2
Stochastic simulators are increasingly used to expand the frontier of scientific knowledge and inform decision-making across real-world contexts. Simulator calibration, a process b…
Staying on Track: Efficient Trajectory Discovery with Adaptive Batch Sampling
Arindam Fadikar, Abby Stevens, Mickael Binois +3
Bayesian optimization (BO) is a powerful framework for estimating parameters of expensive simulation models, particularly in settings where the likelihood is intractable and evalua…
Scaled Block Vecchia Approximation for High-Dimensional Gaussian Process Emulation on GPUs
Qilong Pan, Sameh Abdulah, Mustafa Abduljabbar +8
Emulating computationally intensive scientific simulations is crucial for enabling uncertainty quantification, optimization, and informed decision-making at scale. Gaussian Process…
Gearing Gaussian process modeling and sequential design towards stochastic simulators
Mickael Binois, Arindam Fadikar, Abby Stevens
This chapter presents specific aspects of Gaussian process modeling in the presence of complex noise. Starting from the standard homoscedastic model, various generalizations from t…
Towards Improved Uncertainty Quantification of Stochastic Epidemic Models Using Sequential Monte Carlo
Arindam Fadikar, Abby Stevens, Nicholson Collier +6
Sequential Monte Carlo (SMC) algorithms represent a suite of robust computational methodologies utilized for state estimation and parameter inference within dynamical systems, part…
Developing Distributed High-performance Computing Capabilities of an Open Science Platform for Robust Epidemic Analysis
Nicholson Collier, Justin M. Wozniak, Abby Stevens +6
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among domain expert…
Trajectory-oriented optimization of stochastic epidemiological models
Arindam Fadikar, Mickael Binois, Nicholson Collier +3
Epidemiological models must be calibrated to ground truth for downstream tasks such as producing forward projections or running what-if scenarios. The meaning of calibration change…
Machine learning synthetic spectra for probabilistic redshift estimation: SYTH-Z
Nesar Ramachandra, Jonás Chaves-Montero, Alex Alarcon +3
Photometric redshift estimation algorithms are often based on representative data from observational campaigns. Data-driven methods of this type are subject to a number of potentia…
Scalable Statistical Inference of Photometric Redshift via Data Subsampling
Arindam Fadikar, Stefan M. Wild, Jonas Chaves-Montero
Handling big data has largely been a major bottleneck in traditional statistical models. Consequently, when accurate point prediction is the primary target, machine learning models…
Analyzing Stochastic Computer Models: A Review with Opportunities
Evan Baker, Pierre Barbillon, Arindam Fadikar +10
In modern science, computer models are often used to understand complex phenomena, and a thriving statistical community has grown around analyzing them. This review aims to bring a…
Calibrating a Stochastic Agent Based Model Using Quantile-based Emulation
Arindam Fadikar, Dave Higdon, Jiangzhuo Chen +3
In a number of cases, the Quantile Gaussian Process (QGP) has proven effective in emulating stochastic, univariate computer model output (Plumlee and Tuo, 2014). In this paper, we…