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CATEGORIES:College of Engineering,Thesis/Dissertations
DESCRIPTION:EAS Doctoral Dissertation Defense by by Guancheng Zhou Date: We
 dnesday May 6, 2026 Time: 10:30am Topic: Towards Diagnosis, Fairness, and 
 interpretation of Machine Learning Algorithms Location: Library 314 Abstra
 ct: A number of machine learning algorithms have delivered superior empiri
 cal performance. However, the understanding of their mechanisms has been h
 ampered by the black-box nature of the algorithms. In this proposal, we ap
 proach the problem from two diï¬€erent lens. One is visualization, with a 
 data-driven geometry following kernelâ€”the rpf-kernel, which can extract 
 complex and highly nonlinear patterns beyond the usual principal component
  analysis. The other is the diagnosis perspective. Specifically, we perfor
 m a diagnostic analysis to data points under a given algorithm and hope to
  use this as a proxy to understand the algorithm. Random Forests classific
 ation is used as an example algorithm for our study. We borrow two metrics
 , leverage and influence, from statistics regression to measure the import
 ance of data points, while extending their definition to a small neighborh
 ood of data points. Also studied is a related issue of fairnessâ€”whether 
 the algorithm delivers a response that is fair in terms of some given metr
 ic, for example the gender of the associated subjects. K-means clustering 
 is studied, and a computational eï¬ƒcient post-algorithm adjustment method
  is proposed. Experiments show that the proposed method is eï¬€ective in i
 mproving the fairness while maintaining the clustering performance. Variab
 le importance is of major significance in the practice of statistical anal
 ysis and model interpretation. However, current methods do not consider th
 e correlation between variables, we proposed a method to solve this proble
 m and obtained a more reasonable variable importance. ADVISOR(S): Â Dr. Do
 nghui Yan, Department of Mathematics (dyan@umassd.edu) COMMITTEE MEMBERS: 
  Dr. Haiping Xu, Department of Computer& Information Science Dr. Hongkang 
 Xu, Department of Accounting & Finance Dr. Long Jiao, Department of Comput
 er & Information Science  NOTE:Â  All EAS Students are ENCOURAGED to atten
 d.\nEvent page: /events/cms/eas-doctoral-dissertatio
 n-defense-by-guancheng-zhou.php
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</script><p>EAS Doctoral Dissertation Defen
 se by</p>\n<p>by Guancheng Zhou</p>\n<p>Date: Wednesday May 6\, 2026</p>\n
 <p>Time: 10:30am</p>\n<p>Topic: Towards Diagnosis\, Fairness\, and interpr
 etation of Machine Learning Algorithms</p>\n<p>Location: Library 314</p>\n
 <p>Abstract:</p>\n<p>A number of machine learning algorithms have delivere
 d superior empirical performance. However\, the understanding of their mec
 hanisms has been hampered by the black-box nature of the algorithms. In th
 is proposal\, we approach the problem from two diï¬€erent lens. One is vis
 ualization\, with a data-driven geometry following kernelâ€”the rpf-kernel
 \, which can extract complex and highly nonlinear patterns beyond the usua
 l principal component analysis. The other is the diagnosis perspective. Sp
 ecifically\, we perform a diagnostic analysis to data points under a given
  algorithm and hope to use this as a proxy to understand the algorithm. Ra
 ndom Forests classification is used as an example algorithm for our study.
  We borrow two metrics\, leverage and influence\, from statistics regressi
 on to measure the importance of data points\, while extending their defini
 tion to a small neighborhood of data points. Also studied is a related iss
 ue of fairnessâ€”whether the algorithm delivers a response that is fair in
  terms of some given metric\, for example the gender of the associated sub
 jects. K-means clustering is studied\, and a computational eï¬ƒcient post-
 algorithm adjustment method is proposed. Experiments show that the propose
 d method is eï¬€ective in improving the fairness while maintaining the clu
 stering performance. Variable importance is of major significance in the p
 ractice of statistical analysis and model interpretation. However\, curren
 t methods do not consider the correlation between variables\, we proposed 
 a method to solve this problem and obtained a more reasonable variable imp
 ortance.</p>\n<p>ADVISOR(S): Â Dr. Donghui Yan\, Department of Mathematics
 </p>\n<p>(dyan@umassd.edu)</p>\n<p>COMMITTEE MEMBERS:</p>\n<ul>\n<li>Dr. H
 aiping Xu\, Department of Computer& Information Science</li>\n<li>Dr. Hong
 kang Xu\, Department of Accounting & Finance</li>\n<li>Dr. Long Jiao\, Dep
 artment of Computer & Information Science</li>\n</ul>\n<p>NOTE:Â  All EAS 
 Students are ENCOURAGED to attend.</p><p>Event page: </a></p></body></html>
DTSTAMP:20260423T100120
DTSTART;TZID=America/New_York:20260506T103000
DTEND;TZID=America/New_York:20260506T123000
LOCATION:LIB 314
SUMMARY;LANGUAGE=en-us:EAS Doctoral Dissertation Defense by Guancheng Zhou
UID:b5ee575500d39dc2f10196f29d56fb1e@www.umassd.edu
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