Simon Mak | DeepAI

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∙ 06/13/2023

ACE: Active Learning for Causal Inference with Expensive Experiments

Experiments are the gold standard for causal inference. In many applicat...

0 Difan Song, et al. ∙

∙ 06/11/2023

Additive Multi-Index Gaussian process modeling, with application to multi-physics surrogate modeling of the quark-gluon plasma

The Quark-Gluon Plasma (QGP) is a unique phase of nuclear matter, theori...

0 Kevin Li, et al. ∙

∙ 02/01/2023

Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery

In many areas of science and engineering, computer simulations are widel...

0 Tao Tang, et al. ∙

∙ 11/01/2022

Stacking designs: designing multi-fidelity computer experiments with confidence

In an era where scientific experiments can be very costly, multi-fidelit...

0 Chih-Li Sung, et al. ∙

∙ 09/27/2022

Multi-Stage Multi-Fidelity Gaussian Process Modeling, with Application to Heavy-Ion Collisions

In an era where scientific experimentation is often costly, multi-fideli...

0 Yi Ji, et al. ∙

∙ 03/08/2022

PERCEPT: a new online change-point detection method using topological data analysis

Topological data analysis (TDA) provides a set of data analysis tools fo...

0 Xiaojun Zheng, et al. ∙

∙ 09/15/2021

BacHMMachine: An Interpretable and Scalable Model for Algorithmic Harmonization for Four-part Baroque Chorales

Algorithmic harmonization - the automated harmonization of a musical pie...

13 Yunyao Zhu, et al. ∙

∙ 07/31/2021

A graphical Gaussian process model for multi-fidelity emulation of expensive computer codes

We present a novel Graphical Multi-fidelity Gaussian Process (GMGP) mode...

0 Yi Ji, et al. ∙

∙ 07/09/2021

Gaussian Process Subspace Regression for Model Reduction

Subspace-valued functions arise in a wide range of problems, including p...

6 Ruda Zhang, et al. ∙

∙ 02/27/2021

Online High-Dimensional Change-Point Detection using Topological Data Analysis

Topological Data Analysis (TDA) is a rapidly growing field, which studie...

0 Xiaojun Zheng, et al. ∙

∙ 02/10/2021

Sequential change-point detection for mutually exciting point processes over networks

We present a new CUSUM procedure for sequentially detecting change-point...

0 Haoyun Wang, et al. ∙

∙ 01/17/2021

TSEC: a framework for online experimentation under experimental constraints

Thompson sampling is a popular algorithm for solving multi-armed bandit ...

15 Simon Mak, et al. ∙

∙ 01/05/2021

Bayesian Uncertainty Quantification for Low-rank Matrix Completion

We consider the problem of uncertainty quantification for an unknown low...

0 Henry Shaowu Yuchi, et al. ∙

∙ 12/26/2020

Population Quasi-Monte Carlo

Monte Carlo methods are widely used for approximating complicated, multi...

0 Chaofan Huang, et al. ∙

∙ 06/12/2020

Uncertainty Quantification for Inferring Hawkes Networks

Multivariate Hawkes processes are commonly used to model streaming netwo...

0 Haoyun Wang, et al. ∙

∙ 04/29/2020

Energy Balancing of Covariate Distributions

Bias in causal comparisons has a direct correspondence with distribution...

0 Jared D. Huling, et al. ∙

∙ 11/17/2019

A hierarchical expected improvement method for Bayesian optimization

Expected improvement (EI) is one of the most popular Bayesian optimizati...

0 Zhehui Chen, et al. ∙

∙ 11/14/2019

Distributional Clustering: A distribution-preserving clustering method

One key use of k-means clustering is to identify cluster prototypes whic...

0 Arvind Krishna, et al. ∙

∙ 10/12/2019

Adaptive design for Gaussian process regression under censoring

A key objective in engineering problems is to predict an unknown experim...

0 Jialei Chen, et al. ∙

∙ 10/03/2019

Function-on-function kriging, with applications to 3D printing of aortic tissues

3D-printed medical phantoms, which use synthetic metamaterials to mimic ...

0 Jialei Chen, et al. ∙

∙ 08/23/2019

BdryGP: a new Gaussian process model for incorporating boundary information

Gaussian processes (GPs) are widely used as surrogate models for emulati...

0 Liang Ding, et al. ∙

∙ 02/24/2018

Kernel-smoothed proper orthogonal decomposition (KSPOD)-based emulation for prediction of spatiotemporally evolving flow dynamics

This interdisciplinary study, which combines machine learning, statistic...

0 Yu-Hung Chang, et al. ∙

∙ 12/10/2017

Analysis-of-marginal-Tail-Means - a new method for robust parameter optimization

This paper presents a novel method, called Analysis-of-marginal-Tail-Mea...

0 Simon Mak, et al. ∙

∙ 12/08/2017

Maximum entropy low-rank matrix recovery

We propose a novel, information-theoretic method, called MaxEnt, for eff...

0 Simon Mak, et al. ∙

∙ 07/26/2017

Data-Driven Analysis and Common Proper Orthogonal Decomposition (CPOD)-Based Spatio-Temporal Emulator for Design Exploration

The present study proposes a data-driven framework trained with high-fid...

0 Shiang-Ting Yeh, et al. ∙

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