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05/02/2023
Expressive Mortality Models through Gaussian Process Kernels
We develop a flexible Gaussian Process (GP) framework for learning the c...
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05/10/2022
Large Scale Probabilistic Simulation of Renewables Production
We develop a probabilistic framework for joint simulation of short-term ...
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04/18/2022
On Parametric Optimal Execution and Machine Learning Surrogates
We investigate optimal execution problems with instantaneous price impac...
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11/12/2021
Joint Models for Cause-of-Death Mortality in Multiple Populations
We investigate jointly modeling Age-specific rates of various causes of ...
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09/11/2021
Large-scale local surrogate modeling of stochastic simulation experiments
Gaussian process (GP) regression in large-data contexts, which often ari...
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12/01/2020
mlOSP: Towards a Unified Implementation of Regression Monte Carlo Algorithms
We introduce mlOSP, a computational template for Machine Learning for Op...
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10/16/2020
KrigHedge: GP Surrogates for Delta Hedging
We investigate a machine learning approach to option Greeks approximatio...
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03/19/2020
Adaptive Batching for Gaussian Process Surrogates with Application in Noisy Level Set Estimation
We develop adaptive replicated designs for Gaussian process metamodels o...
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03/05/2020
Multi-Output Gaussian Processes for Multi-Population Longevity Modeling
We investigate joint modeling of longevity trends using the spatial stat...
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06/30/2018
Probabilistic Bisection with Spatial Metamodels
Probabilistic Bisection Algorithm performs root finding based on knowled...
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10/09/2017
Replication or exploration? Sequential design for stochastic simulation experiments
In this paper we investigate the merits of replication, and provide meth...
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09/03/2015
Sequential Design for Ranking Response Surfaces
We propose and analyze sequential design methods for the problem of rank...
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09/16/2013