HGA: A Hardware-Based Genetic Algorithm. - Stephen D. Scott 001; Ashok Samal; Sharad C. Seth (FPGA, 1995)

A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geormetric Patterns. - Sally A. Goldman; Stephen D. Scott 001 (ICML, 1996)

PAC Learning of One-Dimensional Patterns. - Paul W. Goldberg; Sally A. Goldman; Stephen D. Scott 001 (Mach. Learn., 1996)

Learning from Examples with Unspecified Attribute Values (Extended Abstract). - Sally A. Goldman; Stephen Kwek; Stephen D. Scott 001 (COLT, 1997)

Agnostic Learning of Geometric Patterns (Extended Abstract). - Sally A. Goldman; Stephen Kwek; Stephen D. Scott 001 (COLT, 1997)

TCP Dynamic Acknowledgment Delay: Theory and Practice (Extended Abstract). - Daniel R. Dooly; Sally A. Goldman; Stephen D. Scott 001 (STOC, 1998)

A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geometric Patterns. - Sally A. Goldman; Stephen D. Scott 001 (Mach. Learn., 1999)

Efficiently Approximating Weighted Sums with Exponentially Many Terms. - Deepak Chawla; Lin Li; Stephen Scott 001 (COLT/EuroCOLT, 2001)

On-line analysis of the TCP acknowledgment delay problem. - Daniel R. Dooly; Sally A. Goldman; Stephen D. Scott 001 (J. ACM, 2001)

Agnostic Learning of Geometric Patterns. - Sally A. Goldman; Stephen Kwek; Stephen D. Scott 001 (J. Comput. Syst. Sci., 2001)

A novel fiber delay line buffering architecture for optical packet switching. - Lin Li; Stephen D. Scott 001; Jitender S. Deogun (GLOBECOM, 2003)

Multiple-Instance Learning of Real-Valued Geometric Patterns. - Sally A. Goldman; Stephen D. Scott 001 (Ann. Math. Artif. Intell., 2003)

Learning from examples with unspecified attribute values. - Sally A. Goldman; Stephen Kwek; Stephen D. Scott 001 (Inf. Comput., 2003)

A Faster Algorithm for Generalized Multiple-Instance Learning. - Qingping Tao; Stephen D. Scott 001 (FLAIRS, 2004)

SVM-based generalized multiple-instance learning via approximate box counting. - Qingping Tao; Stephen Donald Scott; N. V. Vinodchandran; Thomas Takeo Osugi (ICML, 2004)

EMPRR: a high-dimensional EM-based peicewise regression algorithm. - Manimozhiyan Arumugam; Stephen D. Scott 001 (ICMLA, 2004)

Creating an SVM to play strong poker. - Todd Blank; Leen-Kiat Soh; Stephen Scott 001 (ICMLA, 2004)

LASSO: a learning architecture for semantic web ontologies. - Christopher N. Hammack; Stephen D. Scott 001 (ICMLA, 2004)

Agnostic learning of general geometric patterns and multi-instance learning in Rd. - Stephen D. Scott 001 (ICMLA, 2004)

An Extended Kernel for Generalized Multiple-Instance Learning. - Qingping Tao; Stephen Scott 001; N. V. Vinodchandran; Thomas Takeo Osugi; Brandon Mueller (ICTAI, 2004)

New Techniques for Generation and Analysis of Evolutionary Trees. - Chang Wang 001; Stephen D. Scott 001; Qingping Tao; Dmitri E. Fomenko; Vadim N. Gladyshev (METMBS, 2004)

On approximating weighted sums with exponentially many terms. - Deepak Chawla; Lin Li; Stephen Scott 001 (J. Comput. Syst. Sci., 2004)

Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning. - Thomas Takeo Osugi; Kun Deng; Stephen Scott 001 (ICDM, 2005)

New kernels for protein structural motif discovery and function classification. - Chang Wang 001; Stephen D. Scott 001 (ICML, 2005)

On Generalized Multiple-instance Learning. - Stephen Scott 001; Jun Zhang; Joshua Brown 002 (Int. J. Comput. Intell. Appl., 2005)

Active Learning to Maximize Area Under the ROC Curve. - Matt Culver; Kun Deng; Stephen Scott 001 (ICDM, 2006)

Bandit-Based Algorithms for Budgeted Learning. - Kun Deng; Chris Bourke; Stephen Scott 001; Julie Sunderman; Yaling Zheng (ICDM, 2007)

On reoptimizing multi-class classifiers. - Chris Bourke; Kun Deng; Stephen D. Scott 001; Robert E. Schapire; N. V. Vinodchandran (Mach. Learn., 2008)

Improved MCMC sampling methods for estimating weighted sums in Winnow with application to DNF learning. - Qingping Tao; Stephen D. Scott 001 (Mach. Learn., 2008)

Kernels for Generalized Multiple-Instance Learning. - Qingping Tao; Stephen D. Scott 001; N. V. Vinodchandran; Thomas Takeo Osugi; Brandon Mueller (IEEE Trans. Pattern Anal. Mach. Intell., 2008)

Renaissance computing: an initiative for promoting student participation in computing. - Leen-Kiat Soh; Ashok Samal; Stephen D. Scott 001; Stephen Ramsay; Etsuko Moriyama; George Meyer; Brian Moore 002; William G. Thomas; Duane F. Shell (SIGCSE, 2009)

Active Learning from Multiple Noisy Labelers with Varied Costs. - Yaling Zheng; Stephen Scott 001; Kun Deng (ICDM, 2010)

Multi-Instance Learning. - Soumya Ray; Stephen Scott 001; Hendrik Blockeel (Encyclopedia of Machine Learning, 2010)

Selecting the Appropriate Consistency Algorithm for CSPs Using Machine Learning Classifiers. - Daniel J. Geschwender; Shant Karakashian; Robert J. Woodward; Berthe Y. Choueiry; Stephen D. Scott 001 (AAAI, 2013)

New algorithms for budgeted learning. - Kun Deng; Yaling Zheng; Chris Bourke; Stephen Scott 001; Julie Masciale (Mach. Learn., 2013)

Yeast pheromone pathway modeling using Petri nets. - Abhishek Majumdar; Stephen D. Scott 001; Jitender S. Deogun; Steven Harris (BMC Bioinform., 2014)

Genetic Algorithm Classifier System for Semi-Supervised Learning. - Lee Dee Miller; Leen-Kiat Soh; Stephen Scott 001 (Comput. Intell., 2015)

Computing triangle and open-wedge heavy-hitters in large networks. - Aduri Pavan; Paul Quint; Stephen D. Scott 001; N. V. Vinodchandran; J. Smith (IEEE BigData, 2016)

Learning Hierarchically Decomposable Concepts with Active Over-Labeling. - Yuji Mo; Stephen D. Scott 001; Doug Downey (ICDM, 2016)

Constrained Group Testing to Predict Binding Response of Candidate Compounds. - Paul Quint; Stephen D. Scott 001; N. V. Vinodchandran; Bradley Worley (SDM, 2016)

Multi-Instance Learning. - Soumya Ray; Stephen Scott 001; Hendrik Blockeel (Encyclopedia of Machine Learning and Data Mining, 2017)

Multiple-Instance Learning. - Soumya Ray; Stephen Scott 001; Hendrik Blockeel (Encyclopedia of Machine Learning and Data Mining, 2017)

Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks. - Zhongyuan Zhao 002; Mehmet C. Vuran; Fujuan Guo; Stephen D. Scott 001 (CoRR, 2018)

Elucidation of MicroRNA-Gene Regulation in Human Cancer with Integrative Network Models. - Haluk Dogan; Zeynep Hakguder; Stephen Scott 001; Juan Cui (BIBM, 2019)

Formal Language Constraints for Markov Decision Processes. - Eleanor Quint; Dong Xu 021; Haluk Dogan; Zeynep Hakguder; Stephen Scott 001; Matthew B. Dwyer (CoRR, 2019)

Comparison of Object Detection and Patch-Based Classification Deep Learning Models on Mid- to Late-Season Weed Detection in UAV Imagery. - Arun Narenthiran Veeranampalayam Sivakumar; Jiating Li; Stephen Scott 001; Eric Psota; Amit J. Jhala; Joe D. Luck; Yeyin Shi (Remote. Sens., 2020)

Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning. - Haluk Dogan; Zeynep Hakguder; Roland Madadjim; Stephen Scott 001; Massimiliano Pierobon; Juan Cui (Briefings Bioinform., 2021)

Maize Tassel Detection From UAV Imagery Using Deep Learning. - Aziza Alzadjali; Mohammed H. Alali; Arun Narenthiran Veeranampalayam Sivakumar; Jitender S. Deogun; Stephen Scott 001; James C. Schnable; Yeyin Shi (Frontiers Robotics AI, 2021)

Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-Valued Convolutional Networks. - Zhongyuan Zhao 002; Mehmet Can Vuran; Fujuan Guo; Stephen D. Scott 001 (IEEE J. Sel. Areas Commun., 2021)

Bayesian Deep Structured Semantic Model for Sub-Linear-Time Information Retrieval. - Yuji Mo; Stephen D. Scott 001 (ICDM, 2022)

Self-Supervised Learning in the Twilight of Noisy Real-World Datasets. - Atharva Tendle; Andrew Little; Stephen D. Scott 001; Mohammad Rashedul Hasan (ICMLA, 2022)