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Kevin A. Gluck

dblp:31/4488 · DBLP profile ↗
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19ranked-venue papers
6as first author
0since 2021 · last 2020
0000-0002-7969-0803ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 18 · 6 first-authorArtificial intelligence and machine learning · 16 · 4 first-authorHuman-computer interaction and ubiquitous computing · 2 · 2 first-authorSystems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 77% Performance modeling and evaluation · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed systems › grid computing
volunteer computing
0.112010
Simultaneous performance exploration and optimized search with volunteer computing · HPDC 2010
Performance modeling and evaluation
parameter space exploration
0.012010
Simultaneous performance exploration and optimized search with volunteer computing · HPDC 2010

Methods — techniques the papers use, named apart from their topics

volunteer computing · 0.1stochastic optimization · 0.1
YearPublicationVenuePosition
2020 Strategy Inference and Switch Detection Method Generalizes to Category Learning
Alexander Hough, Kevin A. Gluck, Michael D. Lee 0001
CogSci2
2019 Integrating Methods to Improve Model-based Performance Prediction
Michael G. Collins, Kevin A. Gluck
CogSci2
2019 Deconvolving a Complex, Real-Life Task: Do standard lab tasks predict CPR learning and retention?
Sarah C. Maaß, Florian Sense, Michael Krusmark, Kevin A. Gluck, Hedderik van Rijn
CogSci4
2018 Using Bayesian Hierarchical Modeling and DataShop to Inform Parameter Estimation with the Predictive Performance Equation
Michael G. Collins, Kevin A. Gluck
CogSci2
2017 Using Prior Data to Inform Initial Performance Predictions of Individual Students
Michael G. Collins, Kevin A. Gluck, Matthew M. Walsh, Michael Krusmark
CogSci2
2016 Using Prior Data to Inform Model Parameters in the Predictive Performance Equation
Michael G. Collins, Kevin A. Gluck, Matthew M. Walsh, Michael Krusmark, Glenn Gunzelmann
CogSci2
2016 Instance-based Learning in Multi-cue Diagnosis
Christopher W. Myers, Kevin A. Gluck, Andrew Halsey, Jack Harris
CogSci2
2015 Communicating Cognitive Science: Improving Awareness and Understanding Among People Who are Not Ourselves
Kevin A. Gluck, Wayne D. Gray, Marsha C. Lovett, Arthur B. Markman, Jim Spohrer
CogSci1
2015 Verbal Reports Reveal Strategies in Multiple-Cue Probabilistic Inference
Matthew M. Walsh, Michael G. Collins, Kevin A. Gluck
CogSci3
2014 Computational Cognition Ideation Challenge
Kevin A. Gluck, Vladislav Daniel Veksler, Norbou Buchler, Troy D. Kelley, Paul Bello, James J. Donlon, Alonso Vera, Bob Lee
CogSci1
2014 Controlling State-Space Explosion in Chunk Learning
Vladislav Daniel Veksler, Kevin A. Gluck, Christopher W. Myers, Jack Harris, Thomas Mielke
CogSci2
2014 Using Process Measures to Track Strategy Use in Probabilistic Inference
Matthew M. Walsh, Kevin A. Gluck
CogSci2
2013 Explorations in Human and Machine Learning of Decision Trees
Kevin A. Gluck, Jack Harris, Thomas Mielke, Christopher W. Myers, Vladislav Daniel Veksler
CogSci1
2013 Making Robust Classification Decisions: Constructing and Evaluating Fast and Frugal Trees (FFTs)
Hansjörg Neth, Uwe Czienskowski, Lael J. Schooler, Kevin A. Gluck
CogSci4
2012 Annual Cognitive Modeling Competition
Kevin A. Gluck
CogSci1
2012 An Integrated Model of Associative and Reinforcement Learning
Vladislav Daniel Veksler, Christopher W. Myers, Kevin A. Gluck
CogSci3
2010 Simultaneous performance exploration and optimized search with volunteer computing
abstract
Volunteer computing is a powerful platform for solving complex scientific problems. [email protected] is a volunteer computing project available to the cognitive modeling community for conducting research to better understand the human mind. We are interested in optimizing search processes on volunteer resources, yet we are also interested in exploring and understanding changes in model performance across interacting, non-linear mechanisms and parameter spaces. To support both of these goals, we have developed a stochastic optimization approach and integrated it with [email protected] We tested this approach with a cognitive model on a sample parameter space, demonstrating significant decreases in computational resource utilization and search runtime, while also providing useful visual representations of performance surfaces. Future work will focus on scaling the technique to more volunteers and larger parameter spaces, as well as optimizing the performance of the search algorithm in regards to the challenges inherent with volunteer computing.
L. Richard Moore Jr., Matthew Kopala, Thomas Mielke, Michael Krusmark, Kevin A. Gluck
HPDC5
2000 Broader Bandwidth in Student Modeling: What if ITS were "Eye"TS?
Kevin A. Gluck, John R. Anderson, Scott Douglass
Intelligent Tutoring Systems1
1998 Deconstructing a Computer-Based Tutor: Striving for Better Learning Efficiency in Stat Lady
Kevin A. Gluck, Valerie J. Shute, John R. Anderson, Marsha C. Lovett
Intelligent Tutoring Systems1