Bayesian Stratified Sampling to Assess Corpus Utility. - Judith Hochberg; Clint Scovel; Timothy Thomas; Sam Hall (VLC@COLING/ACL, 1998)

On the VC Dimension of Bounded Margin Classifiers. - Don R. Hush; Clint Scovel (Mach. Learn., 2001)

Machine Learning with Data Dependent Hypothesis Classes. - Adam Cannon; J. Mark Ettinger; Don R. Hush; Clint Scovel (J. Mach. Learn. Res., 2002)

Polynomial-Time Decomposition Algorithms for Support Vector Machines. - Don R. Hush; Clint Scovel (Mach. Learn., 2003)

Selecting a restoration technique to minimize OCR error. - Mike Cannon; Mike Fugate; Don R. Hush; Clint Scovel (IEEE Trans. Neural Networks, 2003)

Density Level Detection is Classification. - Ingo Steinwart; Don R. Hush; Clint Scovel (NIPS, 2004)

Fast Rates to Bayes for Kernel Machines. - Ingo Steinwart; Clint Scovel (NIPS, 2004)

Fat-Shattering of Affine Functions. - Don R. Hush; Clint Scovel (Comb. Probab. Comput., 2004)

Fast Rates for Support Vector Machines. - Ingo Steinwart; Clint Scovel (COLT, 2005)

A Classification Framework for Anomaly Detection. - Ingo Steinwart; Don R. Hush; Clint Scovel (J. Mach. Learn. Res., 2005)

Function Classes That Approximate the Bayes Risk. - Ingo Steinwart; Don R. Hush; Clint Scovel (COLT, 2006)

An Oracle Inequality for Clipped Regularized Risk Minimizers. - Ingo Steinwart; Don R. Hush; Clint Scovel (NIPS, 2006)

QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines. - Don R. Hush; Patrick Kelly; Clint Scovel; Ingo Steinwart (J. Mach. Learn. Res., 2006)

An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels. - Ingo Steinwart; Don R. Hush; Clint Scovel (IEEE Trans. Inf. Theory, 2006)

Gaps in Support Vector Optimization. - Nikolas List; Don R. Hush; Clint Scovel; Ingo Steinwart (COLT, 2007)

Stability of Unstable Learning Algorithms. - Don R. Hush; Clint Scovel; Ingo Steinwart (Mach. Learn., 2007)

Uncorrelated versus independent elliptically-contoured distributions for anomalous change detection in hyperspectral imagery. - James Theiler; Clint Scovel (Computational Imaging, 2009)

Optimal Rates for Regularized Least Squares Regression. - Ingo Steinwart; Don R. Hush; Clint Scovel (COLT, 2009)

Learning from dependent observations. - Ingo Steinwart; Don R. Hush; Clint Scovel (J. Multivar. Anal., 2009)

Radial kernels and their reproducing kernel Hilbert spaces. - Clint Scovel; Don R. Hush; Ingo Steinwart; James Theiler (J. Complex., 2010)

Elliptically Contoured Distributions for Anomalous Change Detection in Hyperspectral Imagery. - James Theiler; Clint Scovel; Brendt Wohlberg; Bernard R. Foy (IEEE Geosci. Remote. Sens. Lett., 2010)

Training SVMs Without Offset. - Ingo Steinwart; Don R. Hush; Clint Scovel (J. Mach. Learn. Res., 2011)

The Optimal Uncertainty Algorithm in the Mystic Framework - Mike McKerns; Houman Owhadi; Clint Scovel; Timothy John Sullivan; Michael Ortiz (CoRR, 2012)

Optimal Uncertainty Quantification. - Houman Owhadi; Clint Scovel; Timothy John Sullivan; Mike McKerns; Michael Ortiz (SIAM Rev., 2013)

Machine Wald. - Houman Owhadi; Clint Scovel (CoRR, 2015)

On the Brittleness of Bayesian Inference. - Houman Owhadi; Clint Scovel; Timothy John Sullivan (SIAM Rev., 2015)

Kernel Mode Decomposition and programmable/interpretable regression networks. - Houman Owhadi; Clint Scovel; Gene Ryan Yoo (CoRR, 2019)

Uncertainty Quantification of the 4th kind; optimal posterior accuracy-uncertainty tradeoff with the minimum enclosing ball. - Hamed Hamze Bajgiran; Pau Batlle Franch; Houman Owhadi; Clint Scovel; Mahdy Shirdel; Michael Stanley; Peyman Tavallali (CoRR, 2021)

Uncertainty quantification of the 4th kind; optimal posterior accuracy-uncertainty tradeoff with the minimum enclosing ball. - Hamed Hamze Bajgiran; Pau Batlle; Houman Owhadi; Mostafa Samir; Clint Scovel; Mahdy Shirdel; Michael Stanley; Peyman Tavallali (J. Comput. Phys., 2022)