Glenn Fung

Publication Activity (10 Years)

Years Active: 2000-2023
Publications (10 Years): 32

Top Topics

Pairwise Comparisons

Machine Learning

Design Space Exploration

Text Classification

Top Venues

Frontiers Appl. Math. Stat.

Proc. VLDB Endow.

Publications

Ronak Mehta, Jeffery Kline, Vishnu Suresh Lokhande, Glenn Fung, Vikas Singh
Efficient Discrete Multi Marginal Optimal Transport Regularization. ICLR (2023)
Saravanan Thirumuruganathan, Han Li, Nan Tang, Mourad Ouzzani, Yash Govind, Derek Paulsen, Glenn Fung, AnHai Doan
Deep Learning for Blocking in Entity Matching: A Design Space Exploration. Proc. VLDB Endow. 14 (11) (2021)
Jaya Krishna Mandivarapu, Eric Bunch, Qian You, Glenn Fung
Efficient Document Image Classification Using Region-Based Graph Neural Network. CoRR (2021)
Teja Kanchinadam, Qian You, Keith Westpfahl, James Kim, Siva Gunda, Sebastian Seith, Glenn Fung
A Simple yet Brisk and Efficient Active Learning Platform for Text Classification. CoRR (2021)
Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh
You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling. CoRR (2021)
Ananth Raj GV, Qian You, Daniel Dickinson, Eric Bunch, Glenn Fung
Document Classification and Information Extraction framework for Insurance Applications. TransAI (2021)
Dan Dickinson, Ananth Raj GV, Glenn Fung
A Model for Zero-shot Text Multi-labeling Using Semantics-based Labels. TransAI (2021)
Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh
Nyströmformer: A Nyström-based Algorithm for Approximating Self-Attention. AAAI (2021)
Eric Bunch, Jeffery Kline, Daniel Dickinson, Suhaas Bhat, Glenn Fung
Weighting vectors for machine learning: numerical harmonic analysis applied to boundary detection. CoRR (2021)
Teja Kanchinadam, Zihang Meng, Joseph Bockhorst, Vikas Singh, Glenn Fung
Graph Neural Networks to Predict Customer Satisfaction Following Interactions with a Corporate Call Center. CoRR (2021)
Zachary Zhou, Jeffery Kline, Devin Conathan, Glenn Fung
Low Resource Quadratic Forms for Knowledge Graph Embeddings. SustaiNLP@EMNLP (2021)
Glenn Fung, Luisa F. Polania, Sou-Cheng T. Choi, Victor Wu, Lawrence Ma
Editorial: Artificial Intelligence in Insurance and Finance. Frontiers Appl. Math. Stat. 7 (2021)
Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention. CoRR (2021)
Jaya Krishna Mandivarapu, Eric Hunch, Glenn Fung
Domain Agnostic Few-Shot Learning For Document Intelligence. CoRR (2021)
Teja Kanchinadam, Keith Westpfahl, Qian You, Glenn Fung
Rationale-based Human-in-the-Loop via Supervised Attention. DaSH@KDD (2020)
Maleeha Qazi, Kaya Tollas, Teja Kanchinadam, Joseph Bockhorst, Glenn Fung
Designing and deploying insurance recommender systems using machine learning. WIREs Data Mining Knowl. Discov. 10 (4) (2020)
Victor Luo, Yazhen Wang, Glenn Fung
SGD Distributional Dynamics of Three Layer Neural Networks. CoRR (2020)
Eric Bunch, Daniel Dickinson, Jeffery Kline, Glenn Fung
Practical applications of metric space magnitude and weighting vectors. CoRR (2020)
Eric Bunch, Qian You, Glenn Fung
Human-In-The-Loop Topic Discovery with Embedded Text Representations. DaSH@KDD (2020)
Shailesh Acharya, Glenn Fung
Using Optimal Embeddings to Learn New Intents with Few Examples: An Application in the Insurance Domain. Converse@KDD (2020)
Sukrat Gupta, Teja Kanchinadam, Devin Conathan, Glenn Fung
Task-Optimized Word Embeddings for Text Classification Representations. Frontiers Appl. Math. Stat. 5 (2019)
Luisa F. Polanía, Glenn Fung, Dongning Wang
Ordinal Regression Using Noisy Pairwise Comparisons for Body Mass Index Range Estimation. WACV (2019)
Shailesh Acharya, Glenn Fung
Mileage Extraction From Odometer Pictures for Automating Auto Insurance Processes. Frontiers Appl. Math. Stat. 5 (2019)
Maleeha Qazi, Srinivas Tunuguntla, Peng Lee, Teja Kanchinadam, Glenn Fung, Neeraj Arora
Discovering Temporal Patterns from Insurance Interaction Data. AAAI (2019)
Teja Kanchinadam, Maleeha Qazi, Joseph Bockhorst, Mary Y. Morell, Katie J. Meissner, Glenn Fung
Using Discriminative Graphical Models for Insurance Recommender Systems. ICMLA (2018)
Luisa F. Polanía, Dongning Wang, Glenn Fung
Ordinal Regression using Noisy Pairwise Comparisons for Body Mass Index Range Estimation. CoRR (2018)
Zihang Meng, Nagesh Adluru, Hyunwoo J. Kim, Glenn Fung, Vikas Singh
Efficient Relative Attribute Learning Using Graph Neural Networks. ECCV (14) (2018)
Yash Govind, Erik Paulson, Palaniappan Nagarajan, Paul Suganthan G. C., AnHai Doan, Youngchoon Park, Glenn Fung, Devin Conathan, Marshall Carter, Mingju Sun
CloudMatcher: A Hands-Off Cloud/Crowd Service for Entity Matching. Proc. VLDB Endow. 11 (12) (2018)
Joseph Bockhorst, Shi Yu, Luisa F. Polanía, Glenn Fung
Predicting Self-reported Customer Satisfaction of Interactions with a Corporate Call Center. ECML/PKDD (3) (2017)
Joe Bockhorst, Yingjian Wang, Sukrat Gupta, Maleeha Qazi, Mingju Sun, Glenn Fung
Using Temporal Discovery and Data-Driven Journey-Maps to Predict Customer Satisfaction. ICMLA (2016)
Glenn Fung, Olvi L. Mangasarian
Unsupervised and Semisupervised Classification Via Absolute Value Inequalities. J. Optim. Theory Appl. 168 (2) (2016)
Ramanathan Subramanian, Rómer Rosales, Glenn Fung, Jennifer G. Dy
Evaluating Crowdsourcing Participants in the Absence of Ground-Truth. CoRR (2016)
Shipeng Yu, Faisal Farooq, Alexander Van Esbroeck, Glenn Fung, Vikram Anand, Balaji Krishnapuram
Predicting readmission risk with institution-specific prediction models. Artif. Intell. Medicine 65 (2) (2015)
Yan Yan, Rómer Rosales, Glenn Fung, Jennifer G. Dy
Active learning from uncertain crowd annotations. Allerton (2014)
Yan Yan, Rómer Rosales, Glenn Fung, Subramanian Ramanathan, Jennifer G. Dy
Learning from multiple annotators with varying expertise. Mach. Learn. 95 (3) (2014)
Glenn Fung, Olvi L. Mangasarian
Privacy-preserving linear and nonlinear approximation via linear programming. Optim. Methods Softw. 28 (1) (2013)
Shipeng Yu, Alexander Van Esbroeck, Faisal Farooq, Glenn Fung, Vikram Anand, Balaji Krishnapuram
Predicting Readmission Risk with Institution Specific Prediction Models. ICHI (2013)
Yan Yan, Rómer Rosales, Glenn Fung, Faisal Farooq, Bharat Rao, Jennifer G. Dy
Active Learning from Multiple Knowledge Sources. AISTATS (2012)
Yan Yan, Rómer Rosales, Glenn Fung, Jennifer G. Dy
Modeling Multiple Annotator Expertise in the Semi-Supervised Learning Scenario CoRR (2012)
Shipeng Yu, Faisal Farooq, Glenn Fung, Balaji Krishnapuram, Alexander Van Esbroeck, Vikram Anand
Building Hospital-Specific Readmission Risk Prediction Models for Heart Failure, Acute Myocardial Infarction and Pneumonia patients. AMIA (2012)
Glenn Fung, O. L. Mangasarian
. J. Optim. Theory Appl. 151 (1) (2011)
Yan Yan, Rómer Rosales, Glenn Fung, Jennifer G. Dy
Active Learning from Crowds. ICML (2011)
Yan Yan, Rómer Rosales, Glenn Fung, Jennifer G. Dy
Modeling Multiple Annotator Expertise in the Semi-Supervised Learning Scenario. UAI (2010)
Mahdokht Masaeli, Yan Yan, Ying Cui, Glenn Fung, Jennifer G. Dy
Convex Principal Feature Selection. SDM (2010)
Yan Yan, Glenn Fung, Jennifer G. Dy, Rómer Rosales
Medical coding classification by leveraging inter-code relationships. KDD (2010)
Yan Yan, Rómer Rosales, Glenn Fung, Mark Schmidt, Gerardo Hermosillo Valadez, Luca Bogoni, Linda Moy, Jennifer G. Dy
Modeling annotator expertise: Learning when everybody knows a bit of something. AISTATS (2010)
Mahdokht Masaeli, Glenn Fung, Jennifer G. Dy
From Transformation-Based Dimensionality Reduction to Feature Selection. ICML (2010)
Andre Dekker, Cary Dehing-Oberije, Dirk De Ruysscher, Philippe Lambin, Kartik Komati, Glenn Fung, Shipeng Yu, Andrew Hope, Wilfried De Neve, Yolande Lievens
Survival Prediction in Lung Cancer Treated with Radiotherapy: Bayesian Networks vs. Support Vector Machines in Handling Missing Data. ICMLA (2009)
Volkan Vural, Glenn Fung, Rómer Rosales, Jennifer G. Dy
Multi-Class Classifiers and their Underlying Shared Structure. IJCAI (2009)
Olvi L. Mangasarian, Edward W. Wild, Glenn Fung
Proximal Knowledge-based Classification. Stat. Anal. Data Min. 1 (4) (2009)
Volkan Vural, Glenn Fung, Balaji Krishnapuram, Jennifer G. Dy, R. Bharat Rao
Using Local Dependencies within Batches to Improve Large Margin Classifiers. J. Mach. Learn. Res. 10 (2009)
Volkan Vural, Glenn Fung, Jennifer G. Dy, Bharat Rao
metric information. Optim. Methods Softw. 23 (4) (2008)
Philippe Bamberger, Isaac Leichter, Nicolas Merlet, Eli Ratner, Glenn Fung, Richard Lederman
Optimizing the CAD Process for Detecting Mammographic Lesions by a New Generation Algorithm Using Linear Classifiers and a Gradient Based Approach. Digital Mammography / IWDM (2008)
Mark Schmidt, Kevin P. Murphy, Glenn Fung, Rómer Rosales
Structure learning in random fields for heart motion abnormality detection. CVPR (2008)
Glenn Fung, Sriram Krishnan, R. Bharat Rao, Hui Chen
Learning Sparse Kernels from 3D Surfaces for Heart Wall Motion Abnormality Detection. AAAI (2008)
R. Bharat Rao, Glenn Fung
On the Dangers of Cross-Validation. An Experimental Evaluation. SDM (2008)
Shipeng Yu, Glenn Fung, Rómer Rosales, Sriram Krishnan, R. Bharat Rao, Cary Dehing-Oberije, Philippe Lambin
Privacy-preserving cox regression for survival analysis. KDD (2008)
Murat Dundar, Glenn Fung, Balaji Krishnapuram, R. Bharat Rao
Multiple-Instance Learning Algorithms for Computer-Aided Detection. IEEE Trans. Biomed. Eng. 55 (3) (2008)
Olvi L. Mangasarian, Edward W. Wild, Glenn Fung
Privacy-preserving classification of vertically partitioned data via random kernels. ACM Trans. Knowl. Discov. Data 2 (3) (2008)
Isaac Leichter, Richard Lederman, Eli Ratner, Nicolas Merlet, Glenn Fung, Balaji Krishnapuram, Philippe Bamberger
Does a Mammography CAD Algorithm with Varying Filtering Levels of Detection Marks, Used to Reduce the False Mark Rate, Adversely Affect the Detection of Small Masses?. Digital Mammography / IWDM (2008)
Mark Schmidt, Glenn Fung, Rómer Rosales
Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches. ECML (2007)
Maleeha Qazi, Glenn Fung, Sriram Krishnan, Rómer Rosales, Harald Steck, R. Bharat Rao, Don Poldermans, Dhanalakshmi Chandrasekaran
Automated Heart Wall Motion Abnormality Detection from Ultrasound Images Using Bayesian Networks. IJCAI (2007)
R. Bharat Rao, Jinbo Bi, Glenn Fung, Marcos Salganicoff, Nancy Obuchowski, David P. Naidich
LungCAD: a clinically approved, machine learning system for lung cancer detection. KDD (2007)
Glenn Fung, Rómer Rosales, R. Bharat Rao
Feature Selection and Kernel Design via Linear Programming. IJCAI (2007)
Glenn Fung, Jonathan Stoeckel
SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information. Knowl. Inf. Syst. 11 (2) (2007)
Glenn Fung, Renaud Seigneuric, Sriram Krishnan, R. Bharat Rao, Brad G. Wouters, Philippe Lambin
Reducing a Biomarkers List via Mathematical Programming: Application to Gene Signatures to Detect Time-Dependent Hypoxia in Cancer. ICMLA (2007)
Glenn Fung, O. L. Mangasarian
Breast Tumor Susceptibility to Chemotherapy Via Support Vector Machines. Comput. Manag. Sci. 3 (2) (2006)
Rómer Rosales, Glenn Fung
Learning sparse metrics via linear programming. KDD (2006)
Glenn Fung, Murat Dundar, Balaji Krishnapuram, R. Bharat Rao
Multiple Instance Learning for Computer Aided Diagnosis. NIPS (2006)
Volkan Vural, Glenn Fung, Balaji Krishnapuram, Jennifer G. Dy, R. Bharat Rao
Batch Classification with Applications in Computer Aided Diagnosis. ECML (2006)
Glenn Fung, Balaji Krishnapuram, Nicolas Merlet, Eli Ratner, Philippe Bamberger, Jonathan Stoeckel, R. Bharat Rao
Addressing Image Variability While Learning Classifiers for Detecting Clusters of Micro-calcifications. Digital Mammography / IWDM (2006)
Jinbo Bi, Senthil Periaswamy, Kazunori Okada, Toshiro Kubota, Glenn Fung, Marcos Salganicoff, R. Bharat Rao
Computer aided detection via asymmetric cascade of sparse hyperplane classifiers. KDD (2006)
Glenn Fung, Olvi L. Mangasarian
Multicategory Proximal Support Vector Machine Classifiers. Mach. Learn. 59 (1-2) (2005)
Jinbo Bi, Glenn Fung, Murat Dundar, R. Bharat Rao
Semi-Supervised Mixture of Kernels via LPBoost Methods. ICDM (2005)
Glenn Fung, Maleeha Qazi, Sriram Krishnan, Jinbo Bi, R. Bharat Rao, A. Katz
Sparse classifiers for Automated HeartWall Motion Abnormality Detection. ICMLA (2005)
Glenn Fung, Sathyakama Sandilya, R. Bharat Rao
Rule extraction from linear support vector machines. KDD (2005)
Jonathan Stoeckel, Glenn Fung
SVM Feature Selection for Classification of SPECT Images of Alzheimer's Disease Using Spatial Information. ICDM (2005)
Glenn Fung, Rómer Rosales, Balaji Krishnapuram
Learning Rankings via Convex Hull Separation. NIPS (2005)
Murat Dundar, Glenn Fung, Jinbo Bi, Sathyakama Sandilya, R. Bharat Rao
Sparse Fisher Discriminant Analysis for Computer Aided Detection. SDM (2005)
Glenn Fung, O. L. Mangasarian
A Feature Selection Newton Method for Support Vector Machine Classification. Comput. Optim. Appl. 28 (2) (2004)
Murat Dundar, Glenn Fung, Luca Bogoni, Michael Macari, A. Megibow, R. Bharat Rao
A methodology for training and validating a CAD system and potential pitfalls. CARS (2004)
Pascal Cathier, Senthil Periaswamy, Anna K. Jerebko, Murat Dundar, Jianming Liang, Glenn Fung, Jonathan Stoeckel, T. Venkata, R. Amara, Arun Krishnan, R. Bharat Rao, Alok Gupta, E. Vega, Shaked Laks, A. Megibow, Michael Macari, Luca Bogoni
CAD for polyp detection: an invaluable tool to meet the increasing need for colon-cancer screening. CARS (2004)
Glenn Fung, Murat Dundar, Jinbo Bi, R. Bharat Rao
A fast iterative algorithm for fisher discriminant using heterogeneous kernels. ICML (2004)
Glenn Fung, Olvi L. Mangasarian
Finite Newton method for Lagrangian support vector machine classification. Neurocomputing 55 (1-2) (2003)
Glenn Fung, Olvi L. Mangasarian, Jude W. Shavlik
Knowledge-Based Nonlinear Kernel Classifiers. COLT (2003)
Glenn Fung
The disputed federalist papers: SVM feature selection via concave minimization. Richard Tapia Celebration of Diversity in Computing Conference (2003)
Glenn Fung, Olvi L. Mangasarian
Incremental Support Vector Machine Classification. SDM (2002)
Glenn Fung, Olvi L. Mangasarian, Jude W. Shavlik
Knowledge-Based Support Vector Machine Classifiers. NIPS (2002)
Glenn Fung, Olvi L. Mangasarian, Alexander J. Smola
Minimal Kernel Classifiers. J. Mach. Learn. Res. 3 (2002)
Glenn Fung, Olvi L. Mangasarian
Proximal support vector machine classifiers. KDD (2001)
Glenn Fung, Olvi L. Mangasarian
Data selection for support vector machine classifiers. KDD (2000)