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Stephen D. Scott 0001
Person information
- affiliation: University of Nebraska, Department of Computer Science and Engineering, Lincoln, NE, USA
- affiliation: Washington University, Department of Computer Science, St. Louis, MO, USA
Other persons with the same name
- Stephen Scott — disambiguation page
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2020 – today
- 2022
- [c28]Yuji Mo, Stephen D. Scott:
Bayesian Deep Structured Semantic Model for Sub-Linear-Time Information Retrieval. ICDM 2022: 1125-1130 - [c27]Atharva Tendle, Andrew Little, Stephen D. Scott, Mohammad Rashedul Hasan:
Self-Supervised Learning in the Twilight of Noisy Real-World Datasets. ICMLA 2022: 461-464 - 2021
- [j18]Haluk Dogan, Zeynep Hakguder, Roland Madadjim, Stephen Scott, Massimiliano Pierobon, Juan Cui:
Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning. Briefings Bioinform. 22(6) (2021) - [j17]Aziza Alzadjali, Mohammed H. Alali, Arun Narenthiran Veeranampalayam Sivakumar, Jitender S. Deogun, Stephen Scott, James C. Schnable, Yeyin Shi
:
Maize Tassel Detection From UAV Imagery Using Deep Learning. Frontiers Robotics AI 8: 600410 (2021) - [j16]Zhongyuan Zhao
Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-Valued Convolutional Networks. IEEE J. Sel. Areas Commun. 39(8): 2407-2420 (2021) - 2020
- [j15]Arun Narenthiran Veeranampalayam Sivakumar, Jiating Li
Comparison of Object Detection and Patch-Based Classification Deep Learning Models on Mid- to Late-Season Weed Detection in UAV Imagery. Remote. Sens. 12(13): 2136 (2020)
2010 – 2019
- 2019
- [c26]Haluk Dogan, Zeynep Hakguder, Stephen Scott, Juan Cui:
Elucidation of MicroRNA-Gene Regulation in Human Cancer with Integrative Network Models. BIBM 2019: 2729-2734 - [i2]Eleanor Quint, Dong Xu, Haluk Dogan, Zeynep Hakguder, Stephen Scott, Matthew B. Dwyer:
Formal Language Constraints for Markov Decision Processes. CoRR abs/1910.01074 (2019) - 2018
- [i1]Zhongyuan Zhao, Mehmet C. Vuran, Fujuan Guo, Stephen D. Scott:
Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks. CoRR abs/1810.07181 (2018) - 2017
- [r3]Soumya Ray, Stephen Scott, Hendrik Blockeel:
Multi-Instance Learning. Encyclopedia of Machine Learning and Data Mining 2017: 864-875 - [r2]Soumya Ray, Stephen Scott, Hendrik Blockeel:
Multiple-Instance Learning. Encyclopedia of Machine Learning and Data Mining 2017: 882-892 - 2016
- [c25]Aduri Pavan, Paul Quint, Stephen D. Scott, N. V. Vinodchandran, J. Smith:
Computing triangle and open-wedge heavy-hitters in large networks. IEEE BigData 2016: 998-1005 - [c24]Yuji Mo, Stephen D. Scott, Doug Downey:
Learning Hierarchically Decomposable Concepts with Active Over-Labeling. ICDM 2016: 340-349 - [c23]Paul Quint, Stephen D. Scott, N. V. Vinodchandran, Bradley Worley:
Constrained Group Testing to Predict Binding Response of Candidate Compounds. SDM 2016: 756-764 - 2015
- [j14]Lee Dee Miller, Leen-Kiat Soh, Stephen Scott
Genetic Algorithm Classifier System for Semi-Supervised Learning. Comput. Intell. 31(2): 201-232 (2015) - 2014
- [j13]Abhishek Majumdar, Stephen D. Scott
Yeast pheromone pathway modeling using Petri nets. BMC Bioinform. 15(S-7): S13 (2014) - 2013
- [j12]Kun Deng, Yaling Zheng, Chris Bourke, Stephen Scott
New algorithms for budgeted learning. Mach. Learn. 90(1): 59-90 (2013) - [c22]Daniel J. Geschwender, Shant Karakashian, Robert J. Woodward, Berthe Y. Choueiry, Stephen D. Scott:
Selecting the Appropriate Consistency Algorithm for CSPs Using Machine Learning Classifiers. AAAI 2013: 1611-1612 - 2010
- [c21]Yaling Zheng, Stephen Scott, Kun Deng:
Active Learning from Multiple Noisy Labelers with Varied Costs. ICDM 2010: 639-648 - [r1]Soumya Ray, Stephen Scott, Hendrik Blockeel:
Multi-Instance Learning. Encyclopedia of Machine Learning 2010: 701-710
2000 – 2009
- 2009
- [c20]Leen-Kiat Soh, Ashok Samal, Stephen D. Scott, Stephen Ramsay, Etsuko Moriyama, George Meyer, Brian Moore, William G. Thomas, Duane F. Shell
Renaissance computing: an initiative for promoting student participation in computing. SIGCSE 2009: 59-63 - 2008
- [j11]Chris Bourke, Kun Deng, Stephen D. Scott, Robert E. Schapire, N. V. Vinodchandran:
On reoptimizing multi-class classifiers. Mach. Learn. 71(2-3): 219-242 (2008) - [j10]Qingping Tao, Stephen D. Scott:
Improved MCMC sampling methods for estimating weighted sums in Winnow with application to DNF learning. Mach. Learn. 73(2): 107-132 (2008) - [j9]Qingping Tao, Stephen D. Scott, N. V. Vinodchandran, Thomas Takeo Osugi, Brandon Mueller:
Kernels for Generalized Multiple-Instance Learning. IEEE Trans. Pattern Anal. Mach. Intell. 30(12): 2084-2098 (2008) - 2007
- [c19]Kun Deng, Chris Bourke, Stephen Scott, Julie Sunderman, Yaling Zheng:
Bandit-Based Algorithms for Budgeted Learning. ICDM 2007: 463-468 - 2006
- [c18]Matt Culver, Kun Deng, Stephen Scott:
Active Learning to Maximize Area Under the ROC Curve. ICDM 2006: 149-158 - 2005
- [j8]Stephen Scott, Jun Zhang, Joshua Brown:
On Generalized Multiple-instance Learning. Int. J. Comput. Intell. Appl. 5(1): 21-36 (2005) - [c17]Thomas Takeo Osugi, Kun Deng, Stephen Scott:
Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning. ICDM 2005: 330-337 - [c16]Chang Wang, Stephen D. Scott:
New kernels for protein structural motif discovery and function classification. ICML 2005: 940-947 - 2004
- [j7]Deepak Chawla, Lin Li, Stephen Scott:
On approximating weighted sums with exponentially many terms. J. Comput. Syst. Sci. 69(2): 196-234 (2004) - [c15]Qingping Tao, Stephen D. Scott:
A Faster Algorithm for Generalized Multiple-Instance Learning. FLAIRS 2004: 550-555 - [c14]Qingping Tao, Stephen Donald Scott, N. V. Vinodchandran, Thomas Takeo Osugi:
SVM-based generalized multiple-instance learning via approximate box counting. ICML 2004 - [c13]Christopher N. Hammack, Stephen D. Scott:
LASSO: a learning architecture for semantic web ontologies. ICMLA 2004: 10-17 - [c12]Todd Blank, Leen-Kiat Soh, Stephen Scott:
Creating an SVM to play strong poker. ICMLA 2004: 150-155 - [c11]Stephen D. Scott:
Agnostic learning of general geometric patterns and multi-instance learning in Rd. ICMLA 2004: 192-199 - [c10]Manimozhiyan Arumugam, Stephen D. Scott:
EMPRR: a high-dimensional EM-based peicewise regression algorithm. ICMLA 2004: 264-271 - [c9]Qingping Tao, Stephen Scott, N. V. Vinodchandran, Thomas Takeo Osugi, Brandon Mueller:
An Extended Kernel for Generalized Multiple-Instance Learning. ICTAI 2004: 272-277 - [c8]Chang Wang, Stephen D. Scott, Qingping Tao, Dmitri E. Fomenko, Vadim N. Gladyshev:
New Techniques for Generation and Analysis of Evolutionary Trees. METMBS 2004: 283-292 - 2003
- [j6]Sally A. Goldman, Stephen D. Scott:
Multiple-Instance Learning of Real-Valued Geometric Patterns. Ann. Math. Artif. Intell. 39(3): 259-290 (2003) - [j5]Sally A. Goldman, Stephen Kwek, Stephen D. Scott:
Learning from examples with unspecified attribute values. Inf. Comput. 180(2): 82-100 (2003) - [c7]Lin Li, Stephen D. Scott, Jitender S. Deogun:
A novel fiber delay line buffering architecture for optical packet switching. GLOBECOM 2003: 2809-2813 - 2001
- [j4]Daniel R. Dooly, Sally A. Goldman, Stephen D. Scott:
On-line analysis of the TCP acknowledgment delay problem. J. ACM 48(2): 243-273 (2001) - [j3]Sally A. Goldman, Stephen Kwek, Stephen D. Scott:
Agnostic Learning of Geometric Patterns. J. Comput. Syst. Sci. 62(1): 123-151 (2001) - [c6]Deepak Chawla, Lin Li, Stephen Scott:
Efficiently Approximating Weighted Sums with Exponentially Many Terms. COLT/EuroCOLT 2001: 82-98
1990 – 1999
- 1999
- [j2]Sally A. Goldman, Stephen D. Scott:
A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geometric Patterns. Mach. Learn. 37(1): 5-49 (1999) - 1998
- [c5]Daniel R. Dooly, Sally A. Goldman, Stephen D. Scott:
TCP Dynamic Acknowledgment Delay: Theory and Practice (Extended Abstract). STOC 1998: 389-398 - 1997
- [c4]Sally A. Goldman, Stephen Kwek, Stephen D. Scott:
Learning from Examples with Unspecified Attribute Values (Extended Abstract). COLT 1997: 231-242 - [c3]Sally A. Goldman, Stephen Kwek, Stephen D. Scott:
Agnostic Learning of Geometric Patterns (Extended Abstract). COLT 1997: 325-333 - 1996
- [j1]Paul W. Goldberg
PAC Learning of One-Dimensional Patterns. Mach. Learn. 25(1): 51-70 (1996) - [c2]Sally A. Goldman, Stephen D. Scott:
A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geormetric Patterns. ICML 1996: 191-199 - 1995
- [c1]Stephen D. Scott, Ashok Samal, Sharad C. Seth:
HGA: A Hardware-Based Genetic Algorithm. FPGA 1995: 53-59
Coauthor Index
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