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Publications

Publications Table of Contents

-Conferences
-Abstracts and Posters
-Workshops
-Theses
-Journals and Book Chapters
-ArXiv Reports
-More...




Contact a member of the group to request a publication. 

All papers tagged with an ACM icon are downloadable at no cost by using the link from this page.

Name note: Articles by Dr. O'Reilly

Dr. O'Reilly consistently publishes under the name Una-May O'Reilly with the abbreviation U.M. O'Reilly. However there are numerous citations of her papers which are inconsistent in citing her name. Popular errors (try to spot them!) are Una May O'Reilly, Una-May O Reilly, OReilly, Una M, U-M or U.-M. or U-M. with many combinations of the preceding errors which mangle the hyphen or apostrophe. We have even found that different fonts have different apostrophes so that two equivalent citations are not attributed to the same paper!

A few sources have incomplete but somewhat extensive lists of my publications:


 

Conferences

2016
  1. Analysis of Locality-Sensitive Hashing for Fast Critical Event Prediction on Physiological Time Series. Y. Bryce Kim and Una-May O'Reilly. 38th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016, Accepted.
  2. Stratified Locality-Sensitive Hashing for Accelerate Physiological Time-Series Retrieval. Y. Bryce Kim, Erik Hemberg, and Una-May O'Reilly. 38th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016, Accepted.

 

2015
  1. Data Science Foundry for MOOCs. Sebastien Boyer, Ben U. Gelman, Benjamin Schreck, Kalyan Veeramachaneni. IEEE/ACM Data Science and Advanced Analytics Conference.
  2. Deep Feature Synthesis: Towards Automating Data Science Endeavors. James Max Kanter, Kalyan Veeramachaneni. Accepted, IEEE/ACM Data Science and Advanced Analytics Conference.
  3. Large-Scale Physiological Waveform Retrieval via Locality-Sensitive Hashing. Y. Bryce Kim and Una-May O'Reilly. 37th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015.
  4. Gaussian Process-based Feature Selection for Wavelet Parameters: Predicting Acute Hypotensive Episodes from Physiological Signals. Franck Dernoncourt, Kalyan Veeramachaneni and Una-May O'Reilly. IEEE 28th International Symposium on Computer-Based Medical Systems. IEEE Computer Society, 2015.
  5. Computer Aided Tax Evasion Policy Analysis: Directed Search using Autonomous Agents. Jacob Rosen, Erik Hemberg, Geoffrey Warner, Sanith Wijesinghe, Una-May O'Reilly, Shadow 2015 , Munster, Germany.
  6. Tax non-Compliance Detection Using Co-Evolution of Tax Evasion Risk and Audit Likelihood. Erik Hemberg, Jacob Rosen, Geoffrey Warner, Sanith Wijesinghe, Una-May O'Reilly, International Conference on AI and Law, 2015, San Diego. Awarded Peter Jackson Award for Best Innovative Application Paper.
  7. Feature Factory: Crowd-Sourced Feature Discovery. Kalyan Veeramachaneni, Una-May O’Reilly, Kiarash Adl, Learning at Scale: L@S 2015: 373-376.
  8. Copula Graphical Models for Wind Resource Estimation. Kalyan Veeramachaneni, Alfredo Cuesta-Infante, Una-May O'Reilly, IJCAI 2015: 2646-265.
  9. Transfer Learning for Predictive Models in Massive Open Online Courses. Sebastien Boyer and Kalyan Veeramachaneni, 17th International Conference in Artificial Intelligence in Education, 2015.
  10. Autotuning Algorithmic Choice for Input Sensitivity. Yufei Ding, Jason Ansel, Kalyan Veeramachaneni, Xipeng Shen, Una-May O’Reilly, Saman Amarasinghe, PLDI 2015.
  11.  Building Predictive Models via Feature Synthesis. Ignacio Arnaldo, Una-May O'Reilly, Kalyan Veeramachaneni. GECCO '15 Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015.

 


2014
  1. Building a Stage 1 Computer Aided Detector for Breast Cancer using Genetic Programming. Conor Ryan, Krzysztof Krawiec, Una-May O'Reilly, Jeannie Fitzgerald, David Medernach.17th European Conference on Genetic Programming, Springer LNCS 8599, pp 162-173.
  2. Behavioral Search Drivers for Genetic Programing. Krzysztof Krawiec, Una-May O'Reilly. 17th European Conference on Genetic Programming, Springer LNCS 8599, pp 210-221.
  3. Flash: A GP-GPU Ensemble Learning System for handling Large Datasets. Ignacio Arnaldo, Kalyan Veeramachaneni and Una-May O'Reilly. 17th European Conference on Genetic Programming, Springer LNCS 8599, pp 13-24.
  4. OpenTuner: An Extensible Framework for Program Autotuning. Jason Ansel, Shoaib Kamil, Kalyan Veeramachaneni, Jonathan Ragan-Kelley, Jeffrey Bosboom, Una-May O'Reilly and Saman P. Amarasinghe. International Conference on Parallel Architectures and Compilation, PACT '14, pp 303--316, DOI: 10.1145/2628071.2628092. Available free from the ACM Digital Library.
  5. Large-Scale Methodological Comparison of Acute Hypotensive Episode Forecasting Using MIMIC2 Physiological Waveforms. Y. Bryce Kim, Joohyun Seo, Una-May O'Reilly. 2014 Proceedings of the IEEE 27th International Symposium on Computer-Based Medical Systems (CBMS), New York, May 2014.
  6. Multiple Regression Genetic Programming. Ignacio Arnaldo, Krzysztof Krawiec, Una-May O'Reilly. GECCO '14, pp 879--886. Available free from the ACM Digital Library.
  7. Behavioral Programming: A Broader and More Detailed Take on Semantic GP. Krzysztof Krawiec, Una-May O'Reilly. GECCO 2014, pp 935-94, Awarded Best Paper in the Genetic Programming TrackAvailable free from the ACM Digital Library.
  8. A Continuous Developmental Model for Wind Farm Layout Optimization. Sylvain Cussat-Blanc, Hervé Luga, Una-May O'Reilly, Kalyan Veeramachaneni, Dennis Wilson. GECCO 2014, pp745-752. Available free from the ACM Digital Library.

 

2013
  1. Introducing Graphical Models to Analyze Genetic Programming Dynamics. Erik Hemberg, Constantin Berzan, Kalyan Veeramachaneni, Una-May O'Reilly. FOGA XII '13 Proceedings of the twelfth workshop on Foundations of genetic algorithms XII, 2013.
  2. Cloud Scale Distributed Evolutionary Strategies for High Dimensional Problems. Dennis Wilson, Kalyan Veeramachaneni, and Una-May O'Reilly. EVOPAR track, Applications of Evolutionary Computation, Lecture Notes in Computer Science Volume 7835, 2013, pp 519-528.
  3. Cloud Driven Design of a Distributed Genetic Programming Platform. Owen Derby, Kalyan Veeramachaneni, and Una-May O'Reilly. EVOPAR track, Applications of Evolutionary Computation, Lecture Notes in Computer Science Volume 7835, 2013, pp 509-518.
  4. Learning regression ensembles with genetic programming at scale. Kalyan Veeramachaneni, Owen Derby, Dylan Sherry, Una-May O'Reilly. GECCO '13, Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference, 2013.
  5. On Learning to Generate Wind Farm Layouts. Dennis Wilson, Emmanuel Awa, Sylvain Cussat-Blanc, Kalyan Veeramachaneni, Una-May O'Reilly. GECCO '13, Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference, 2013.
  6. Modeling Tax Evasion with Genetic Algorithms. Geoffrey Warner, Sanith Wijesinghe , Uma Marques, Una-May O'Reilly, Erik Hemberg, Osama Badar. Shadow 2013, Munster.

 

2012
  1. Flex- GP: Genetic Programming on the Cloud. D. Sherry, K. Veeramachaneni, J. McDermott, and U.M. O’Reilly. In EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, EvoSTOC, LNCS, Vol. 7248, pp. 477-486, Springer Verlag, 11-13 April 2012. (This paper was in the EvoPAR conference. It was awarded Best Paper.) 
  2. Hyperparameter Tuning in Bandit-Based Adaptive Operator Selection. Maciej Pacula and Jason Ansel and Saman Amarasinghe and Una-May O'Reilly.Applications of Evolutionary Computing, EvoApplications2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, EvoSTOC, LNCS, Vol. 7248, pp. 71-80, Springer Verlag, 11-13 April 2012.
  3. Genetic Programming Needs Better Benchmarks. James McDermott, David R. White, Sean Luke, Luca Manzoni, Mauro Castelli, Leonardo Vanneschi, Wojciech Jaskowski, Krzysztof Krawiec, Robin Harper, Kenneth De Jong, and Una-May O’Reilly. In Proceedings of GECCO 2012, Philadelphia, 2012. ACM. 
  4.  An Investigation of Local Patterns for Estimation of Distribution Genetic Programming.E. Hemberg, K. Veeramachaneni, J. McDermott, C. Berzan, and U-M. O’Reilly.  In Proceedings of GECCO 2012, Philadelphia, 2012. ACM. 
  5. The Max Problem Revisited: The Importance of Mutation in Genetic Programming. Timo Kotzing, Andrew M. Sutton, Frank Neumann and Una-May O'Reilly. GECCO '12 Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, 2012. 
  6. Optimizing Energy Output and Layout Costs for Large Wind Farms using Particle Swarm Optimization. Kalyan Veeramachaneni, Markus Wagner, Una-May O'Reilly and Frank Neumann. 2012 IEEE Congress on Evolutionary Computation.
  7. Siblingrivalry: Online Autotuning Through Local Competitions.[http://dl.acm.org/author_page.cfm?id=81314480768|]Jason Ansel, Maciej Pacula, Yee Lok Wong, Cy Chan, Marek Olszewski, Una-May O'Reilly, Saman Amarasinghe. CASES '12 Proceedings of the 2012 international conference on Compilers, architectures and synthesis for embedded systems, 2012.

 

2011
  1. How Far Is It From Here to There? A Distance that is Coherent with GP Operators. James McDermott, Leonardo Vanneschi, Kalyan Veeramachaneni, Una-May O'Reilly, Proceedings of 2011 European Conference on Genetic Programming (EuroGP), Springer LNCS.
  2. An Executable Graph Representation for Evolutionary Generative Music. James McDermott, Una-May O'Reilly. Evolutionary Music and Art Track, 2011 Genetic and Evolutionary Computation Conference (GECCO 2011), Dublin, Ireland. July, 2011.
  3. An Efficient Evolutionary Algorithm for Solving Incrementally Structured Problems. Jason Ansel, Maciej Pacula, Saman Amarasinghe, Una-May O'Reilly. Real World Applications track of Genetic and Evolutionary Computation Conference (GECCO 2011), Dublin, Ireland. July, 2011.
  4. Optimizing the Layout of 1000 Wind Turbines. Markus Wagner, Kalyan Veeramachaneni, Frank Neumann, Una-May O'Reilly. Scientific Proceedings of the 2011 meeting of the European Wind Energy Association (EWEA 2011).
  5. Creative transformations: How generative and evolutionary music can inform music HCI. James McDermott, Dylan Sherry, and Una-May O’Reilly. Proceedings of BCS HCI 2011 Workshop – When Words Fail: What Can Music Interaction tel l us about HCI? British Computer Society, 2011.

 

2010
  1. Knowledge Mining with Genetic Programming Methods for Variable Selection in Flavor Design. Katya Vladislavleva, Kalyan Veeramachaneni, Matt Burland, Jason Parcon, Una-May O'Reilly. Genetic and Evolutionary Computation Conference (GECCO), pp. 941-948, ACM, 2010. Genetic programming track.
  2. Evolutionary Optimization of Flavors. Kalyan Veeramachaneni, Katya Vladislavleva, Matt Burland, Jason Parcon, Una-May O'Reilly. Genetic and Evolutionary Computation Conference (GECCO), pp. 1291-1298, ACM, 2010. Real World Applications track.
  3. Feature Extraction from Optimization Data via DataModeler’s Ensemble Symbolic Regression. Kalyan Veeramachaneni, Katya Vladislavleva, Una-May O' Reilly. Learning and Intelligent Optimization (LION), Lecture Notes in Computer Science, Vol. 6073, pp. 251-265, Springer, 2010. (pdf)
  4. Learning a Lot from Only a Little: Genetic Programming for Panel Segmentation on Sparse Sensory Evaluation Data. Katya Vladislavleva, Kalyan Veeramachaneni, Una-May O' Reilly, Matt Burland, Jason Parcon. Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010, LNCS, Vol. 6021, pp. 244-255, Springer, 7-9 April 2010. (pdf)
  5. A Genetic Algorithm to Minimize Chromatic Entropy. Greg Durett, Muriel Medard, and Una-May O'Reilly.10th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP), Lecture Notes in Computer Science, Vol. 6022, pp. 59-70, Springer, 7-9 April 2010. (pdf)


 

Abstracts and Posters

2016

  1. Multi-Resolution Histogram Representation for Physiological Time Series and its Integration into Locality-Sensitive Hashing. Y. Bryce Kim and Una-May O'Reilly. Poster in 38th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016.

2015

  1. Computer Aided Tax Evasion Policy Analysis: Directed Search using Autonomous Agents. Jacob Rosen, Erik Hemberg, Geoffrey Warner, Sanith Wijesinghe, Una-May O'Reilly, Extended Abstract, AAMAS 2015, Istanbul.
  2. Large-Scale Prediction of Acute Hypotensive Episodes via Locality-Sensitive Hashing on Physiological Waveform Time Series. Y. Bryce Kim and Una-May O'Reilly. Poster in 37th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015.


 

Workshops

2016

  1. Dynamics of Adversarial Co-evolution in Tax Non-Compliance Detection. Jacob Rosen, Erik Hemberg, Una-May O’Reilly. GECCO Security & Risk Workshop, 2016. DOI: http://dx.doi.org/10.1145/2908961.2931680
  2. Multi-Line Batch Scheduling by Similarity. Ignacio Arnaldo, Erik Hemberg, Una-May O’Reilly. GECCO Industrial Applications of Metaheuristics Workshop, 2016, DOI: http://dx.doi.org/10.1145/2908961.2931647
  3. Stratified Locality-Sensitive Hashing for Sublinear Time Critical Event Prediction. Y. Bryce Kim, Erik Hemberg, and Una-May O'Reilly. Conference on Neural Information Processing Systems (NIPS) Machine Learning in Healthcare Workshop, 2016.


2015

  1. Analysis of Data-Driven Event Prediction based on Sublinear Time Retrieval of Physiological Waveforms. Y. Bryce Kim and Una-May O'Reilly. Conference on Neural Information Processing Systems (NIPS) Machine Learning in Healthcare Workshop, 2015.


2014

  1. Learning Decision Lists with Lags for Physiological Time Series. Erik Hemberg, Kalyan Veeramachaneni, Babak Hodjat, Prashan Wanigasekara, Hormoz Shahrzad, Una-May O'Reilly. 3rd Workshop on Data Mining for Medicine and Healthcare (2014), April 26, 2014, Philadelphia, PA, (held in conjunction with 14th SIAM International Conference on Data Mining (SDM 2014).


2013

  1. Building MultiClass Nonlinear Classifiers with GPUs. Ignacio Arnaldo, Kalyan Veeramachaneni and Una-May O'Reilly. 2014 NIPS Workshop on Big Learning.
  2. BeatDB : A Large Scale Waveform Feature Repository. Franck Dernoncourt, Kalyan Veeramachaneni and Una-May O'Reilly. MLCDA@NIPS 2013 : Machine Learning for Clinical Data Analysis and Healthcare.
  3. MoocViz: A Large Scale, Open Access, Collaborative Data Analytics Framework for MOOCs. Franck Dernoncourt,Choung Do, Sherif Halawa, Una-May O'Reilly, Colin Taylor, Kalyan Veeramachaneni and Sherwin Wu. DDE@NIPS 2013: Data Directed Education. Workshop website.
  4. Analyzing Millions of Submissions to Help MOOC Instructors Understand Problem Solving. Fang Han, Kalyan Veeramachaneni and Una-May O'Reilly. DDE@NIPS 2013: Data Directed Education. Workshop website.
  5. Copula-Based Wind Resource Assessment. Kalyan Veeramachaneni, Teasha Feldman-Fitzthum, Una-May O’Reilly, Alfredo Cuesta-Infante. Machine Learning for Sustainability Workshop@NIPS 2013.
  6. Efficient Training Set Use For Blood Pressure Prediction in a Large Scale Learning Classifier System. Erik Hemberg, Kalyan Veeramachaneni, Franck Dernoncourt, Mark Wagy and Una-May O'Reilly. Sixteenth International Workshop on Learning Classifiers Systems.
  7. Learning Blood Pressure Behavior from Large Physiological Waveform Repositories. Alexander Waldin, Kalyan Veeramachaneni, Una-May O'Reilly. ICML Workshop on Healthcare 2013.
  8. MOOCdb: Developing Data Standards for MOOC Datascience. Kalyan Veeramachaneni, Franck Dernoncourt, Colin Taylor, Zachary A. Pardos, Una-May O'Reilly. MOOCShop at Artificial Intelligence in Education, 2013. Workshop website.


2012

  1. Autoconstructive Evolution for Structural Problems. Kyle Harrington, Lee Spector, Jordan Pollack and Una-May O'Reilly. 2nd Workshop on Evolutionary Computation for the Automated Design of Algorithms, in Companion Volume of GECCO 2012, Philadelphia, 2012.
  2. Graphical Models and What They Reveal About Genetic Programming. Erik Hemberg, Kalyan Veeramachaneni and Una-May O'Reilly. Symbolic Regression and Modelling Workshop, GECCO 2012, Philadelphia, 2012. Presentation as PDF


 

Theses

2017

  1. Cohort Selection and Sampling Techniques to Balance Time-Series Retrospective Studies. Brian Bell Jr, M.E. thesis, MIT Dept of EECS, February 2017. Advisor: Una-May O'Reilly.
  2. Extensions to Behavioral Genetic Programming. Steven B. Fine, M.E. thesis, MIT Dept of EECS, February 2017. Advisor: Una-May O'Reilly.



2016

  1. Towards An Automatic Predictive Question Formulations. Benjamin J. Schreck, M.E. thesis, MIT Dept of EECS, June 2016. Advisor: Kalyan Veeramachaneni.
  2. Model Factory: A New Way to Look at Data Through Models. Yonglin Wu, M.E. thesis, MIT Dept of EECS, June 2016. Advisor: Kalyan Veeramachaneni.
  3. Transfer Learning for Predictive Models in MOOCs. Sebastien Boyer, M.S. thesis, MIT Dept of EECS, IDSS, June 2016. Advisor: Kalyan Veeramachaneni.
  4. The Synthetic Data Vault: Generative Modeling for Relational Databases. Neha Patki, M.E. thesis, MIT Dept of EECS, June 2016. Advisor: Kalyan Veeramachaneni.
  5. Articial Intelligence Opportunities and an End-To-End Data-Driven Solution for Predicting Hardware Failures. Mario Orozco Gabriel, M.S., MBA thesis, MIT Dept of Mechanical Engineering, Sloan School of Management, June 2016. Advisors: Kalyan Veeramachaneni, Tauhid Zaman, John J. Leonard.



2015

  1. BeatDB: An End-to-end Approach to Unveil Saliencies from Massive Signal Data Sets. Franck Dernoncourt, S.M, thesis, MIT Dept of EECS, February 2015. Advisors: Una-May O'Reilly, Kalyan Veeramachaneni.
  2. Computer Aided Tax Avoidance Policy Analysis. Jacob Rosen, S.M Thesis, MIT Dept of EDS, 2015. Advisors: Una-May O'Reilly, Erik Hemberg.
  3. Deep Mining: Copula-Based Hyper-Parameter Optimization for Machine Learning Pipelines. Sebastien Dubois M.E. Thesis, Ecole Polytechnique, MIT, 2015. Advisor: Kalyan Veeramachaneni.
  4. Machine Learning Blocks. Bryan Omar Collazo Santiago, M.E. Thesis MIT Dept of EECS, 2015. Advisor: Kalyan Veeramachaneni.
  5. The Data Science Machine: Emulating Human Intelligence in Data Science Endeavors. James Max Kanter, MIT Dept of EECS, 2015. Advisor: Kalyan Veeramachaneni.



2014

  1. PhysioMiner: A Scalable Cloud Based Framework for Physiological Waveform Mining. Vineet Gopal, M.Eng Thesis completed in MIT Dept of EECS, 2014. Advisors: Kalyan Veeramachaneni, Una-May O'Reilly.
  2. Stopout Prediction in Massive Open Online Courses. Colin Taylor, M.Eng Thesis completed in MIT Dept of EECS, 2014. Advisors: Kalyan Veeramachaneni, Una-May O'Reilly.
  3. Modeling Problem Solving in Massive Open Online Courses. Fang Han, M.Eng Thesis completed in MIT Dept of EECS, 2014. Advisors: Kalyan Veeramachaneni, Una-May O'Reilly.
  4. Using Distributed Machine Learning to Predict Arterial Blood Pressure. Ijeoma Emeagwali, M.Eng Thesis completed in MIT Dept of EECS, 2014. Advisors: Una-May O'Reilly, Erik Hemberg.
  5. Simulating Tax Evasion Using Agent Based Modelling And Evolutionary Search. Osama Badar, M.Eng Thesis completed in MIT Dept of EECS, 2014. Advisors: Una-May O'Reilly, Erik Hemberg



2013

  1. Large-scale Consensus Clustering and Data Ownership Considerations for Medical Application. Chidube Ezeozue, S.M, thesis, MIT Dept of EECS, 2013. Advisors: Una-May O'Reilly, Kalyan Veeramachaneni.
  2. FlexGP: A Scalable System for Factored Learning in the Cloud. Owen Derby, M.Eng, thesis, completed in MIT Dept of EECS, 2013. Advisors: Kalyan Veeramachaneni, Una-May O'Reilly.
  3. Learning Blood Pressure Behavior from Large Blood Pressure Waveform Repositories and Building Predictive Models. Alexander Waldin, Masters Thesis, ETH-Zurich, 2013. Advisors: Kalyan Veeramachaneni, Una-May O'Reilly.
  4. FlexGP 2.0: Multiple Levels of Parallelism in Distributed Machine Learning via Genetic Programming. Dylan Sherry, 2013.  Advisors: Kalyan Veeramachaneni, Una-May O'Reilly.


 

Journals and Book Chapters

2016

  1. Detecting Tax Evasion: A Co-Evolutionary Approach. Erik Hemberg, Jacob Rosen, Geoff Warner, Sanith Wijesinghe, Una-May O'Reilly. Artificial Intelligence and Law, 1-34, 2016. DOI 10.1007/s10506-016-9181-6.
  2. Investigating Multi-Population Competitive Coevolution for Anticipating Tax Evasion, Erik Hemberg, Jacob Rosen, Una-May O'Reilly. Genetic Programming: Theory to Practice, 2016.

 

2015

  1. Learning a Goal-Oriented Model for Energy Efficient Adaptive Applications in Data Centers. Monica Vitali, Barbara Pernici, Una-May O'Reilly. Inf. Sci. 319: 152-170 (2015)
  2. FlexGP: Cloud-Based Ensemble Learning with Genetic Programming for Large Regression Problems. Kalyan Veeramachaneni, Ignacio Arnaldo, Owen Derby, Una-May O’Reilly. Journal Of Grid Computing 13(3): 391-407, (Mar 2015).
  3. Bring Your Own Learner! A cloud-based, data-parallel commons for machine learning. Ignacio Arnaldo, Kalyan Veeramachaneni, Andrew Song, Una-May O’Reilly. Special Issue on Computational Intelligence for Cloud Computing, IEEE Comp. Int. Mag. 10(1): 20-32 (2015).
  4. Genetic Programming. James McDermott and Una-May O'Reilly. Handbook of Computational Intelligence, 2015: 845-869 (Part E Evolutionary Computation with Topic Editors: Dr. F. Neumann and Dr. K Witt, Editors in Chief: Prof. Janusz Kacprzyk and Prof. Witold Pedrycz).

 

2014

  1. Modeling tax evasion with genetic algorithms. Geoffrey Warner, Sanith Wijesinghe, Uma Marques, Osama Badar, Jacob Rosen, Erik Hemberg, and Una-May O’Reilly. Economics of Governance, pp 1-14, Nov 2014. DOI: 10.1007/s10101-014-0152-7.
  2. Technology for Mining the Big Data of MOOCs. Una-May O'Reilly, Kalyan Veeramachaneni. Winter 2014, Research and Practice in Assessment.
  3. Using Reinforcement Learning to Optimize Occupant Comfort and Energy Usage in HVAC Systems. Pedro Fazenda, Kalyan Veeramachaneni, Pedro Lima, Una-May O'Reilly. November 2014. Journal of Ambient Intelligence and Smart Environments: JAISE 6(6): 675-690.
  4. The Max Problem Revisited: The Importance of Mutation in Genetic Programming. Timo Koetzing, Andrew M. Sutton, Frank Neumann and Una-May O'Reilly. Theoretical Computer Science, vol 545, pp 94-107, Aug 2014.

 

2013

  1. Techniques for Accurate Wind Resource Estimation by Modeling Statistical Dependency. K. Veeramachaneni, Xiang Ye, U.M. O'Reilly. Ch 10, pp 303-330 in Computational Intelligent Data Analysis for Sustainable Development, Editors: Ting Yu, Nitesh Chawla, Simeon Simoff, Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, Taylor & Francis, 2013. ISBN 9781439895948. Link to Book
  2. Better GP Benchmarks: Community Survey Results and Proposals. David R. White, James McDermott, Mauro Castelli, Luca Manzoni, Brian W. Goldman, Gabriel Kronberger, Wojciech Jaśkowski, Una-May O'Reilly, Sean Luke. Genetic Programming and Evolvable Machines, Volume 14 Issue 1, March 2013, Pages 3-29. DOI: 10.1007/s10710-012-9177-2.
  3. Maintenance of a Long Running Distributed Genetic Programming System For Solving Problems Requiring Big Data. Babak Hodjat, Erik Hemberg, Hormoz Shahrzad and Una-May O'Reilly. Genetic Programming Theory and Practice XI, pp65-83. DOI: 10.1007/978-1-4939-0375-7_4.

 

2012

  1. Evolutionary and Generative Music Informs Music HCI—and Vice Versa. James McDermott, Dylan Sherry, and Una-May O’Reilly. Kate Wilkie, Simon Holland, Paul Mulholland, and Allan Seago, editors, Music Interaction. Springer, 2012. forthcoming.
  2. EC-Star: A Massive-Scale, Hub and Spoke, Distributed Genetic Programming System. Babak Hodjat, Mark Wagy and Una-May O'Reilly. Proceedings of Genetic Programming X: From Theory to Practice, Kluwer, 2012. Pre-camera version PDF
  3. FlexGP.py: Prototyping Flexibly-Scaled, Flexibly-Factored Genetic Programming for the Cloud. James McDermott, Kalyan Veeramachaneni and Una-May O'Reilly. Proceedings of Genetic Programming X: From Theory to Practice, Kluwer, 2012. Pre-camera version PDF

 

2011

  1. Computational Complexity Analysis of Genetic Programming - Initial Results and Future Directions. Frank Neumann, Una-May O’Reilly and Markus Wagner. Proceedings of Genetic Programming IX: From Theory to Practice, Editors: Jason Moore, Rick Riolo, Katya Vladaislavleva, Kluwer Press.
  2. Baseline Genetic Programming Symbolic Regression on Benchmarks for Sensory Evaluation Modeling. Pierre-Luc Noel, Kalyan Veeramachaneni, and Una-May O’Reilly, Proceedings of Genetic Programming IX: From Theory to Practice, Editors: Jason Moore, Rick Riolo, Katya Vladaislavleva, Springer Press. Preprint as PDF
  3. Feature Extraction from Optimization Samples Via Ensemble Based Symbolic Regression. Kalyan Veeramachaneni, Ekaterina Vladislavleva, Una-May O’Reilly. Annals of Mathematics and Artificial Intelligence, Springer. Online-first preprint as pdf. 2011, Volume 61, Number 2, Pages 105-123.
  4. Knowledge Mining Sensory Evaluation Data with Genetic Programming, Statistical Techniques, and Swarm Optimization. Kalyan Veeramachaneni, Ekaterina Vladislavleva, Una-May O’Reilly. Genetic Programming and Evolvable Machines 3(1), pp. 103-133, March 2012.

 

2010

  1. Hogs and Slackers: Using Operations Balance in a Genetic Algorithm to Optimize Sparse Algebra Computation on Distributed Architectures. Una-May O'Reilly, Eric Robinson, Sanjeev Mohindra, Julie Mullen, Nadya Bliss. Parallel Computing, Volume 36, Issues 10-11, October-November 2010, Pages 635-644 Special Issue on Parallel Architectures and Bioinspired Algorithms.


 

ArXiv Reports

2014

  1. arXiv#1408.3382 Likely to stop? Predicting Stopout in Massive Open Online Courses. Colin Taylor, Kalyan Veeramachaneni, Una-May O'Reilly.
  2. arXiv#1407.5238 Towards Feature Engineering at Scale for Data from Massive Open Online Courses. Kalyan Veeramachaneni, Una-May O'Reilly, Colin Taylor.
  3. arXiv# 1406.2015 MOOCdb: Developing Standards and Systems to Support MOOC Data Science. Kalyan Veeramachaneni, Sherif Halawa, Franck Dernoncourt, Una-May O'Reilly, Colin Taylor, Chuong Do.


 

More Publications...