Computer Science
Williams College
Selected Papers and Talks for Andrea Danyluk
In Journals:
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Feature Selection vs Theory Reformulation: a Study of Genetic Refinement
of Knowledge-based Neural Networks
Burns, B. and Danyluk A.
Machine Learning, 38:1/2, pp. 89-108, 2000.
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Problem Definition, Data Cleaning, and Evaluation: A Classifier Learning Case
Study
Provost, F. and Danyluk A.
Informatica, 23, pp. 123-136, 1999.
In Refereed Proceedings:
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An MS in CS for non-CS Majors: Moving to Increase Diversity of Thought and Demographics in CS
Brodley, C., Barry, M., Connell, A., Gill, C., Gorton, I., Hescott, B., Lackaye, B., LuBien, C., Razzaq, L., Shesh, A., Williams, T., Danyluk, A.
Proceedings of the ACM SIGCSE Symposium, 2020.
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Predicting Expressive Bow Controls for Violin and Viola
Yu, L. J. and Danyluk, A.
Proceedings of the 6th International Conference on Computational Intelligence in Music, Sound, Art, and Design, 2017.
The final publication will be available at Springer.
PredictingBowControlsData.zip
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Feature Selection via Probabilistic Outputs
Arnosti, N. A. and Danyluk, A.
Proceedings of the 29th International Conference on Machine Learning, 2012.
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Introducing Concurrency in CS1
Bruce, K., Danyluk, A., and Murtagh, T.
Proceedings of the ACM SIGCSE Symposium, 2010.
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Why Structural Recursion Should Be Taught before Arrays in CS1
Bruce, K., Danyluk, A., and Murtagh, T.
Proceedings of the ACM SIGCSE Symposium, 2005.
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Event-driven Programming is Simple Enough for CS1
Bruce, K., Danyluk, A., and Murtagh, T.
The Sixth Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE), pp. 1-4, 2001.
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A library to support a graphics-based object-first approach to CS1
Bruce, K., Danyluk, A., and Murtagh, T.
Proceedings of the ACM SIGCSE Symposium, pp. 6-10, 2001.
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Small Disjuncts in Action: Learning to Diagnose Errors in the Local Loop of the Telephone Network
Proceedings of the Tenth International Conference on Machine Learning, Morgan Kaufmann, pp. 81-88, 1993.
* without figures
In Refereed Workshops:
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Deep Transfer as Structure Learning in Markov Logic Networks
Moore, D. and Danyluk, A.
Proceedings of the Workshop on Statistical Relational AI at AAAI, 2010.
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Off-Topic Detection in Conversational Telephone Speech
Stewart, R., Danyluk, A., and Liu, Y.
Proceedings of the Workshop on Analyzing Conversations in Text and Speech at HLT-NAACL, 2006.
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Event-Driven Programming Facilitates Learning Standard Programming Concepts
OOPSLA '04 Educators Symposium, 2004.
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Using Robotics to Motivate Learning in an AI Course Aimed at Non-Majors
AAAI 2004 Spring Symposium on Accessible Hands-on Artificial Itelligence and Robotics Education, 2004.
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Theory refinement through knowledge-based feature set selection
Burns, B. and Danyluk A.
Proceedings of the Fourth International Workshop on Multistrategy
Learning, Esposito, Michalski, & Saitta (eds), pp. 53-63, 1998.
Technical Reports:
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Predicting the Future: AI Approaches to Time-Series Problems, Papers from
the 1998 Workshop
AAAI Press Technical Report WS-98-07.
Predicting the Future: AI Approaches to Time-Series Problems: a Workshop Report
Danyluk A., Fawcett, T., and Provost, F.
AI Magazine, 20:1, p. 124.
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Learning from Bad Data
Provost, F. and Danyluk, A.
in Working Notes for Applying Machine Learning in Practice: A Workshop at
the Twelfth International Machine Learning Conference, (Technical Report
AIC-95-023) Washington, DC: Naval Research Laboratory, Navy Center for
Applied Research in Artificial Intelligence, Aha, D. and Riddle P. (eds),
pp. 27-33, 1995.
Talks: