Daniel W. Apley

55 papers A* 1C 1Misc 1Journal 51Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	SELDON: Supernova Explosions Learned by Deep ODE Networks. AAAI Jiezhong Wu, Jack O'Brien, Jennifer Li, M. S. Krafczyk, Ved G. Shah, Amanda Wasserman, Daniel W. Apley, Gautham Narayan, Noelle I. Samia
2026	J	`jnl`	SELDON: Supernova Explosions Learned by Deep ODE Networks. CoRR Jiezhong Wu, Jack O'Brien, Jennifer Li, M. S. Krafczyk, Ved G. Shah, Amanda Wasserman, Daniel W. Apley, Gautham Narayan, Noelle I. Samia
2025	J	`jnl`	A Framework for Supervised and Unsupervised Segmentation and Classification of Materials Microstructure Images. CoRR Kungang Zhang, Daniel W. Apley, Wei Chen, Wing K. Liu, L. Catherine Brinson
2025	J	`jnl`	Fractional Cross-Validation for Optimizing Hyperparameters of Supervised Learning Algorithms. Technometrics Suraj Yerramilli, Daniel W. Apley
2024	J	`jnl`	Coefficient tree regression: fast, accurate and interpretable predictive modeling. Mach. Learn. Özge Sürer, Daniel W. Apley, Edward C. Malthouse
2024	J	`jnl`	Discovering interpretable structure in longitudinal predictors via coefficient trees. Adv. Data Anal. Classif. Özge Sürer, Daniel W. Apley, Edward C. Malthouse
2024	J	`jnl`	Emerging Microelectronic Materials by Design: Navigating Combinatorial Design Space with Scarce and Dispersed Data. CoRR Hengrui Zhang, Alexandru B. Georgescu, Suraj Yerramilli, Christopher Karpovich, Daniel W. Apley, Elsa A. Olivetti, James M. Rondinelli, Wei Chen
2024	Misc	`conf`	Plausible Inference with a Plausible Lipschitz Constant. WSC Gregory Keslin, Daniel W. Apley, Barry L. Nelson
2024	J	`jnl`	Simulation-Free Determination of Microstructure Representative Volume Element Size via Fisher Scores. CoRR Wei Liu, Satyajit Mojumder, Wing Kam Liu, Wei Chen, Daniel W. Apley
2023	J	`jnl`	Concept Drift Monitoring and Diagnostics of Supervised Learning Models via Score Vectors. Technometrics Kungang Zhang, Anh Tuan Bui, Daniel W. Apley
2023	J	`jnl`	Fully Bayesian Inference for Latent Variable Gaussian Process Models. SIAM/ASA J. Uncertain. Quantification Suraj Yerramilli, Akshay Iyer, Wei Chen, Daniel W. Apley
2023	J	`jnl`	Interpretable Architecture Neural Networks for Function Visualization. CoRR Shengtong Zhang, Daniel W. Apley
2023	J	`jnl`	Interpretable Architecture Neural Networks for Function Visualization. J. Comput. Graph. Stat. Shengtong Zhang, Daniel W. Apley
2022	J	`jnl`	Analyzing Nonparametric Part-to-Part Variation in Surface Point Cloud Data. Technometrics Anh Tuan Bui, Daniel W. Apley
2022	J	`jnl`	Fully Bayesian inference for latent variable Gaussian process models. CoRR Suraj Yerramilli, Akshay Iyer, Wei Chen, Daniel W. Apley
2022	J	`jnl`	Robust monitoring of stochastic textured surfaces. Int. J. Prod. Res. Anh Tuan Bui, Daniel W. Apley
2022	J	`jnl`	Uncertainty-aware Mixed-variable Machine Learning for Materials Design. CoRR Hengrui Zhang, Wei Wayne Chen, Akshay Iyer, Daniel W. Apley, Wei Chen
2021	J	`jnl`	Bias-corrected Estimation of the Density of a Conditional Expectation in Nested Simulation Problems. ACM Trans. Model. Comput. Simul. Ran Yang, David Kent, Daniel W. Apley, Jeremy Staum, David Ruppert
2021	J	`jnl`	Coefficient tree regression for generalized linear models. Stat. Anal. Data Min. Özge Sürer, Daniel W. Apley, Edward C. Malthouse
2021	J	`jnl`	Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors. CoRR Liwei Wang, Suraj Yerramilli, Akshay Iyer, Daniel W. Apley, Ping Zhu, Wei Chen
2020	J	`jnl`	A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors. Technometrics Yichi Zhang, Siyu Tao, Wei Chen, Daniel W. Apley
2020	J	`jnl`	Concept Drift Monitoring and Diagnostics of Supervised Learning Models via Score Vectors. CoRR Kungang Zhang, Anh Tuan Bui, Daniel W. Apley
2020	J	`jnl`	Including a Nugget Effect in Lifted Brownian Covariance Models. SIAM/ASA J. Uncertain. Quantification Yang Yu, Ning Zhang, Daniel W. Apley, Wenxin Jiang
2020	J	`jnl`	Technometrics 2019 Editor's Report. Technometrics Daniel W. Apley
2019	J	`jnl`	An exploratory analysis approach for understanding variation in stochastic textured surfaces. Comput. Stat. Data Anal. Anh Tuan Bui, Daniel W. Apley
2019	J	`jnl`	Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables. CoRR Yichi Zhang, Daniel W. Apley, Wei Chen
2019	J	`jnl`	Projection-free kernel principal component analysis for denoising. Neurocomputing Anh Tuan Bui, Joon-Ku Im, Daniel W. Apley, George C. Runger
2019	J	`jnl`	Technometrics 2018 Editor's Report. Technometrics Daniel W. Apley
2018	J	`jnl`	A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors. CoRR Yichi Zhang, Siyu Tao, Wei Chen, Daniel W. Apley
2018	J	`jnl`	A Monitoring and Diagnostic Approach for Stochastic Textured Surfaces. Technometrics Anh Tuan Bui, Daniel W. Apley
2018	J	`jnl`	Distinct Variation Pattern Discovery Using Alternating Nonlinear Principal Component Analysis. IEEE Trans. Neural Networks Learn. Syst. Phillip Howard, Daniel W. Apley, George Runger
2018	J	`jnl`	Patchwork Kriging for Large-scale Gaussian Process Regression. J. Mach. Learn. Res. Chiwoo Park, Daniel W. Apley
2017	J	`jnl`	Batch Sample Design from Databases for Logistic Regression. Qual. Reliab. Eng. Int. Liwen Ouyang, Daniel W. Apley, Sanjay Mehrotra
2017	J	`jnl`	Lifted Brownian Kriging Models. Technometrics Matthew Plumlee, Daniel W. Apley
2017	J	`jnl`	Patchwork Kriging for Large-scale Gaussian Process Regression. CoRR Chiwoo Park, Daniel W. Apley
2017	J	`jnl`	Technometrics 2017 Editor's Report. Technometrics Daniel W. Apley
2016	J	`jnl`	A design of experiments approach to validation sampling for logistic regression modeling with error-prone medical records. J. Am. Medical Informatics Assoc. Liwen Ouyang, Daniel W. Apley, Sanjay Mehrotra
2016	J	`jnl`	Discovering the Nature of Variation in Nonlinear Profile Data. Technometrics Zhenyu Shi, Daniel W. Apley, George C. Runger
2014	J	`jnl`	Feature selection for noisy variation patterns using kernel principal component analysis. Knowl. Based Syst. Anshuman Sahu, Daniel W. Apley, George C. Runger
2012	J	`jnl`	A time-dependent proportional hazards survival model for credit risk analysis. J. Oper. Res. Soc. Joon-Ku Im, Daniel W. Apley, C. Qi, X. Shan
2012	J	`jnl`	Posterior Distribution Charts: A Bayesian Approach for Graphically Exploring a Process Mean. Technometrics Daniel W. Apley
2012	J	`jnl`	Tangent Hyperplane Kernel Principal Component Analysis for Denoising. IEEE Trans. Neural Networks Learn. Syst. Joon-Ku Im, Daniel W. Apley, George C. Runger
2011	J	`jnl`	Efficient Nested Simulation for Estimating the Variance of a Conditional Expectation. Oper. Res. Yunpeng Sun, Daniel W. Apley, Jeremy Staum
2011	—	`conf`	Image denoising with a multi-phase kernel principal component approach and an ensemble version. AIPR Anshuman Sahu, George C. Runger, Daniel W. Apley
2011	J	`jnl`	Improved design of robust exponentially weighted moving average control charts for autocorrelated processes. Qual. Reliab. Eng. Int. Hyun Cheol Lee, Daniel W. Apley
2010	J	`jnl`	Comment. Technometrics Daniel W. Apley
2008	J	`jnl`	Blind Identification of Manufacturing Variation Patterns by Combining Source Separation Criteria. Technometrics Xuemei Shan, Daniel W. Apley
2006	J	`jnl`	Optimal Design of Second-Order Linear Filters for Control Charting. Technometrics Chang-Ho Chin, Daniel W. Apley
2005	J	`jnl`	A characterization of diagnosability conditions for variance components analysis in assembly operations. IEEE Trans Autom. Sci. Eng. Daniel W. Apley, Yu Ding
2004	J	`jnl`	Cautious Control of Industrial Process Variability With Uncertain Input and Disturbance Model Parameters. Technometrics Daniel W. Apley, Jeong-Bae Kim
2003	J	`jnl`	Design of Exponentially Weighted Moving Average Control Charts for Autocorrelated Processes With Model Uncertainty. Technometrics Daniel W. Apley, Hyun Cheol Lee
2003	J	`jnl`	Identifying Spatial Variation Patterns in Multivariate Manufacturing Processes - A Blind Separation Approach. Technometrics Daniel W. Apley, Ho-Young Lee
2003	C	`conf`	MLPCA Based Logistical Regression Analysis for Pattern Clustering in Manufacturing Processes. IDEAL Feng Zhang, Daniel W. Apley
2001	J	`jnl`	A Factor-Analysis Method for Diagnosing Variability in Mulitvariate Manufacturing Processes. Technometrics Daniel W. Apley, Jianjun Shi
1999	J	`jnl`	An order downdating algorithm for tracking system order and parameters in recursive least squares identification. IEEE Trans. Signal Process. Daniel W. Apley, Jianjun Shi