Recent Publications

A comprehensive list of publications, including preprints, can be found on Google Scholar and DBLP. Keynote Addresses, research talks, and interviews can be found on YouTube.

2024

  1. Hemberg, E., Turner, M. J., Rutar, N., & O’Reilly, U.-M. (2024). Enhancements to Threat, Vulnerability, and Mitigation Knowledge for Cyber Analytics, Hunting, and Simulations. DTRAP, 5(1), 8:1–8:33. https://doi.org/10.1145/3615668
  2. Hemberg, E., Moskal, S., & O’Reilly, U.-M. (2024). Evolving code with a large language model. Genet. Program. Evolvable Mach., 25(2), 21. https://doi.org/10.1007/S10710-024-09494-2
  3. Fajardo, M. A. H., Hemberg, E., Toutouh, J., O’Reilly, U.-M., & Lehre, P. K. (2024). A Self-adaptive Coevolutionary Algorithm. In X. Li & J. Handl (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2024, Melbourne, VIC, Australia, July 14-18, 2024. ACM. https://doi.org/10.1145/3638529.3654132
  4. Hemberg, E., O’Reilly, U.-M., & Toutouh, J. (2024). Cooperative Coevolutionary Spatial Topologies for Autoencoder Training. In X. Li & J. Handl (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2024, Melbourne, VIC, Australia, July 14-18, 2024. ACM. https://doi.org/10.1145/3638529.3654127
  5. Jorgensen, S., Nadizar, G., Pietropolli, G., Manzoni, L., Medvet, E., O’Reilly, U.-M., & Hemberg, E. (2024). Large Language Model-based Test Case Generation for GP Agents. In X. Li & J. Handl (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2024, Melbourne, VIC, Australia, July 14-18, 2024. ACM. https://doi.org/10.1145/3638529.3654056
  6. O’Reilly, U.-M. (2024). Coevolution in Natural and Artificial Systems. In X. Li & J. Handl (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2024, Melbourne, VIC, Australia, July 14-18, 2024. ACM. https://doi.org/10.1145/3638529.3663651
  7. O’Reilly, U.-M., & Hemberg, E. (2024). Using Large Language Models for Evolutionary Search. In X. Li & J. Handl (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2024, Melbourne, VIC, Australia, July 14-18, 2024 (pp. 973–983). ACM. https://doi.org/10.1145/3638530.3648432
  8. Toutouh, J., & O’Reilly, U.-M. (2024). Coevolutionary Computation for Adversarial Deep Learning. In X. Li & J. Handl (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2024, Melbourne, VIC, Australia, July 14-18, 2024 (pp. 1410–1431). ACM. https://doi.org/10.1145/3638530.3648405
  9. Hemberg, E., Moskal, S., & O’Reilly, U.-M. (2024). Evolving Code with A Large Language Model. CoRR, abs/2401.07102. https://doi.org/10.48550/ARXIV.2401.07102

2023

  1. Toutouh, J., Nalluru, S., Hemberg, E., & O’Reilly, U.-M. (2023). Semi-supervised generative adversarial networks with spatial coevolution for enhanced image generation and classification. Appl. Soft Comput., 148, 110890. https://doi.org/10.1016/J.ASOC.2023.110890
  2. Shashkov, A., Hemberg, E., Tulla, M., & O’Reilly, U.-M. (2023). Adversarial agent-learning for cybersecurity: a comparison of algorithms. Knowl. Eng. Rev., 38, e3. https://doi.org/10.1017/S0269888923000012
  3. Gold, R., Branquinho, H., Hemberg, E., O’Reilly, U.-M., & Garcı́a-Sánchez Pablo. (2023). Genetic Programming and Coevolution to Play the Bomberman™ Video Game. In J. Correia, S. L. Smith, & R. Qaddoura (Eds.), Applications of Evolutionary Computation - 26th European Conference, EvoApplications 2023, Held as Part of EvoStar 2023, Brno, Czech Republic, April 12-14, 2023, Proceedings (Vol. 13989, pp. 765–779). Springer. https://doi.org/10.1007/978-3-031-30229-9_49
  4. Toutouh, J., Nalluru, S., Hemberg, E., & O’Reilly, U.-M. (2023). Semi-Supervised Learning with Coevolutionary Generative Adversarial Networks. In S. Silva & Paquete Luı́s (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2023, Lisbon, Portugal, July 15-19, 2023 (pp. 568–576). ACM. https://doi.org/10.1145/3583131.3590426
  5. O’Reilly, U.-M., & Hemberg, E. (2023). Genetic Programming: A Tutorial Introduction. In S. Silva & Paquete Luı́s (Eds.), Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023 (pp. 1026–1036). ACM. https://doi.org/10.1145/3583133.3595061
  6. Lehre, P. K., Fajardo, M. A. H., Toutouh, J., Hemberg, E., & O’Reilly, U.-M. (2023). Analysis of a Pairwise Dominance Coevolutionary Algorithm And DefendIt. In S. Silva & Paquete Luı́s (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2023, Lisbon, Portugal, July 15-19, 2023 (pp. 1027–1035). ACM. https://doi.org/10.1145/3583131.3590411
  7. Toutouh, J., & O’Reilly, U.-M. (2023). Coevolutionary Computation for Adversarial Deep Learning. In S. Silva & Paquete Luı́s (Eds.), Companion Proceedings of the Conference on Genetic and Evolutionary Computation, GECCO 2023, Companion Volume, Lisbon, Portugal, July 15-19, 2023 (pp. 1379–1398). ACM. https://doi.org/10.1145/3583133.3595049
  8. Fajardo, M. A. H., Lehre, P. K., Toutouh, J., Hemberg, E., & O’Reilly, U.-M. (2023). Analysis of a Pairwise Dominance Coevolutionary Algorithm with Spatial Topology. In S. Winkler, L. Trujillo, C. Ofria, & T. Hu (Eds.), Genetic Programming Theory and Practice XX [GPTP 2023] (pp. 19–44). Springer. https://doi.org/10.1007/978-981-99-8413-8_2
  9. Kong, B. J., Hemberg, E., Bell, A., & O’Reilly, U.-M. (2023). Investigating Student’s Problem-solving Approaches in MOOCs using Natural Language Processing. LAK 2023: 13th International Learning Analytics and Knowledge Conference, LAK2023, Arlington, TX, USA, March 13-17, 2023, 262–272. https://doi.org/10.1145/3576050.3576091
  10. Wang, M., Srikant, S., Samak, M., & O’Reilly, U.-M. (2023). RaceInjector: Injecting Races to Evaluate and Learn Dynamic Race Detection Algorithms. In P. Ferrara & L. Hadarean (Eds.), Proceedings of the 12th ACM SIGPLAN International Workshop on the State Of the Art in Program Analysis, SOAP 2023, Orlando, FL, USA, 17 June 2023 (pp. 63–70). ACM. https://doi.org/10.1145/3589250.3596142
  11. Jia, J., Srikant, S., Mitrovska, T., Gan, C., Chang, S., Liu, S., & O’Reilly, U.-M. (2023). ClawSAT: Towards Both Robust and Accurate Code Models. In T. Zhang, X. Xia, & N. Novielli (Eds.), IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2023, Taipa, Macao, March 21-24, 2023 (pp. 212–223). IEEE. https://doi.org/10.1109/SANER56733.2023.00029
  12. Srikant, S., Ivanova, A. A., Sueoka, Y., Kean, H. H., Dhamala, R., Fedorenko, E., Bers, M. U., & O’Reilly, U.-M. (2023). Program Comprehension Does Not Primarily Rely On the Language Centers of the Human Brain. CoRR, abs/2304.12373. https://doi.org/10.48550/ARXIV.2304.12373
  13. Sankaranarayanan, A., Hemberg, E., & O’Reilly, U.-M. (2023). The Facebook Algorithm’s Active Role in Climate Advertisement Delivery. CoRR, abs/2308.03191. https://doi.org/10.48550/ARXIV.2308.03191
  14. Moskal, S., Laney, S., Hemberg, E., & O’Reilly, U.-M. (2023). LLMs Killed the Script Kiddie: How Agents Supported by Large Language Models Change the Landscape of Network Threat Testing. CoRR, abs/2310.06936. https://doi.org/10.48550/ARXIV.2310.06936

2022

  1. Özlem Özmen Garibay, Yousefi, N., Aslett, K., Baggio, J. A., Hemberg, E., Jayalath, C., Mantzaris, A. V., Miller, B., O’Reilly, U.-M., Rand, W., Senevirathna, C., & Garibay, I. (2022). Entropy-Based Characterization of Influence Pathways in Traditional and Social Media. 8th IEEE International Conference on Collaboration and Internet Computing, CIC 2022, Atlanta, GA, USA, December 14-16, 2022, 38–44. https://doi.org/10.1109/CIC56439.2022.00016
  2. Tseng, S., Hemberg, E., & O’Reilly, U.-M. (2022). Synthesizing Programs from Program Pieces Using Genetic Programming and Refinement Type Checking. In E. Medvet, G. L. Pappa, & B. Xue (Eds.), Genetic Programming - 25th European Conference, EuroGP 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20-22, 2022, Proceedings (Vol. 13223, pp. 197–211). Springer. https://doi.org/10.1007/978-3-031-02056-8_13
  3. Schweim, D., Hemberg, E., Sobania, D., & O’Reilly, U.-M. (2022). Exploiting Knowledge from Code to Guide Program Search. In E. Medvet, G. L. Pappa, & B. Xue (Eds.), Genetic Programming - 25th European Conference, EuroGP 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20-22, 2022, Proceedings (Vol. 13223, pp. 262–277). Springer. https://doi.org/10.1007/978-3-031-02056-8_17
  4. Flores, D., Hemberg, E., Toutouh, J., & O’Reilly, U.-M. (2022). Coevolutionary generative adversarial networks for medical image augumentation at scale. In J. E. Fieldsend & M. Wagner (Eds.), GECCO ’22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, USA, July 9 - 13, 2022 (pp. 367–376). ACM. https://doi.org/10.1145/3512290.3528742
  5. Zhao, Y., Hemberg, E., Derbinsky, N., Mata, G., & O’Reilly, U.-M. (2022). Using domain knowledge in coevolution and reinforcement learning to simulate a logistics enterprise. In J. E. Fieldsend & M. Wagner (Eds.), GECCO ’22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9 - 13, 2022 (pp. 514–517). ACM. https://doi.org/10.1145/3520304.3528990
  6. Turner, M. J., Hemberg, E., & O’Reilly, U.-M. (2022). Analyzing multi-agent reinforcement learning and coevolution in cybersecurity. In J. E. Fieldsend & M. Wagner (Eds.), GECCO ’22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, USA, July 9 - 13, 2022 (pp. 1290–1298). ACM. https://doi.org/10.1145/3512290.3528844
  7. Toutouh, J., & O’Reilly, U.-M. (2022). Coevolutionary computation for adversarial deep learning. In J. E. Fieldsend & M. Wagner (Eds.), GECCO ’22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9 - 13, 2022 (pp. 1487–1505). ACM. https://doi.org/10.1145/3520304.3533651
  8. Moskal, S., Hemberg, E., & O’Reilly, U.-M. (2022). CyberEvo: evolutionary search of knowledge-based behaviors in a cyber attack campaign. In J. E. Fieldsend & M. Wagner (Eds.), GECCO ’22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9 - 13, 2022 (pp. 2168–2176). ACM. https://doi.org/10.1145/3520304.3533999
  9. Gold, R., Grant, A. H., Hemberg, E., Gunaratne, C., & O’Reilly, U.-M. (2022). GUI-based, efficient genetic programming for Unity3D. In J. E. Fieldsend & M. Wagner (Eds.), GECCO ’22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9 - 13, 2022 (pp. 2310–2313). ACM. https://doi.org/10.1145/3520304.3534022
  10. Gold, R., Grant, A. H., Hemberg, E., Gunaratne, C., & O’Reilly, U.-M. (2022). GUI-Based, Efficient Genetic Programming and AI Planning for Unity3D. In L. Trujillo, S. M. Winkler, S. Silva, & W. Banzhaf (Eds.), Genetic Programming Theory and Practice XIX [GPTP 2022] (pp. 57–79). Springer. https://doi.org/10.1007/978-981-19-8460-0_3
  11. Kim, J., O’Reilly, U.-M., & Seok, J. (2022). Computer Code Representation through Natural Language Processing for fMRI Data Analysis. 2022 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022, Jeju Island, Korea, Republic of, February 21-24, 2022, 184–187. https://doi.org/10.1109/ICAIIC54071.2022.9722644
  12. Srikant, S., Lipkin, B., Ivanova, A. A., Fedorenko, E., & O’Reilly, U.-M. (2022). Convergent Representations of Computer Programs in Human and Artificial Neural Networks. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022. http://papers.nips.cc/paper_files/paper/2022/hash/77b5aaf2826c95c98e5eb4ab830073de-Abstract-Conference.html
  13. Jia, J., Srikant, S., Mitrovska, T., Gan, C., Chang, S., Liu, S., & O’Reilly, U.-M. (2022). CLAWSAT: Towards Both Robust and Accurate Code Models. CoRR, abs/2211.11711. https://doi.org/10.48550/ARXIV.2211.11711

2021

  1. Hemberg, E., Toutouh, J., Al-Dujaili, A., Schmiedlechner, T., & O’Reilly, U.-M. (2021). Spatial Coevolution for Generative Adversarial Network Training. ACM Trans. Evol. Learn. Optim., 1(2), 6:1–6:28. https://doi.org/10.1145/3458845
  2. Wick, J., Hemberg, E., & O’Reilly, U.-M. (2021). Getting a Head Start on Program Synthesis with Genetic Programming. In T. Hu, N. Lourenço, & E. Medvet (Eds.), Genetic Programming - 24th European Conference, EuroGP 2021, Held as Part of EvoStar 2021, Virtual Event, April 7-9, 2021, Proceedings (Vol. 12691, pp. 263–279). Springer. https://doi.org/10.1007/978-3-030-72812-0_17
  3. Schweim, D., Hemberg, E., Sobania, D., O’Reilly, U.-M., & Rothlauf, F. (2021). Using knowledge of human-generated code to bias the search in program synthesis with grammatical evolution. In K. Krawiec (Ed.), GECCO ’21: Genetic and Evolutionary Computation Conference, Companion Volume, Lille, France, July 10-14, 2021 (pp. 331–332). ACM. https://doi.org/10.1145/3449726.3459548
  4. Toutouh, J., & O’Reilly, U.-M. (2021). Signal propagation in a gradient-based and evolutionary learning system. In F. Chicano & K. Krawiec (Eds.), GECCO ’21: Genetic and Evolutionary Computation Conference, Lille, France, July 10-14, 2021 (pp. 377–385). ACM. https://doi.org/10.1145/3449639.3459319
  5. O’Reilly, U.-M., & Hemberg, E. (2021). Genetic programming: a tutorial introduction. In K. Krawiec (Ed.), GECCO ’21: Genetic and Evolutionary Computation Conference, Companion Volume, Lille, France, July 10-14, 2021 (pp. 443–453). ACM. https://doi.org/10.1145/3449726.3461394
  6. Shlapentokh-Rothman, M., Kelly, J., Baral, A., Hemberg, E., & O’Reilly, U.-M. (2021). Coevolutionary modeling of cyber attack patterns and mitigations using public datasets. In F. Chicano & K. Krawiec (Eds.), GECCO ’21: Genetic and Evolutionary Computation Conference, Lille, France, July 10-14, 2021 (pp. 714–722). ACM. https://doi.org/10.1145/3449639.3459351
  7. Toutouh, J., & O’Reilly, U.-M. (2021). Coevolutionary computation for adversarial deep learning. In K. Krawiec (Ed.), GECCO ’21: Genetic and Evolutionary Computation Conference, Companion Volume, Lille, France, July 10-14, 2021 (pp. 983–1001). ACM. https://doi.org/10.1145/3449726.3461419
  8. Zhao, Y., Hemberg, E., Derbinsky, N., Mata, G., & O’Reilly, U.-M. (2021). Simulating a logistics enterprise using an asymmetrical wargame simulation with soar reinforcement learning and coevolutionary algorithms. In K. Krawiec (Ed.), GECCO ’21: Genetic and Evolutionary Computation Conference, Companion Volume, Lille, France, July 10-14, 2021 (pp. 1907–1915). ACM. https://doi.org/10.1145/3449726.3463172
  9. Srikant, S., Liu, S., Mitrovska, T., Chang, S., Fan, Q., Zhang, G., & O’Reilly, U.-M. (2021). Generating Adversarial Computer Programs using Optimized Obfuscations. 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. https://openreview.net/forum?id=PH5PH9ZO_4
  10. Shashkov, A., Gold, R., Hemberg, E., Kong, B. J., Bell, A., & O’Reilly, U.-M. (2021). Analyzing Student Reflection Sentiments and Problem-Solving Procedures in MOOCs. In C. Meinel, Pérez-Sanagustı́n Mar, M. Specht, & A. Ogan (Eds.), L@S’21: Eighth ACM Conference on Learning @ Scale, Virtual Event, Germany, June 22-25, 2021 (pp. 247–250). ACM. https://doi.org/10.1145/3430895.3460150
  11. O’Reilly, U.-M., & Devroey, X. (Eds.). (2021). Search-Based Software Engineering - 13th International Symposium, SSBSE 2021, Bari, Italy, October 11-12, 2021, Proceedings (Vol. 12914). Springer. https://doi.org/10.1007/978-3-030-88106-1
  12. Toutouh, J., & O’Reilly, U.-M. (2021). Signal Propagation in a Gradient-Based and Evolutionary Learning System. CoRR, abs/2102.08929. https://arxiv.org/abs/2102.08929
  13. Srikant, S., Liu, S., Mitrovska, T., Chang, S., Fan, Q., Zhang, G., & O’Reilly, U.-M. (2021). Generating Adversarial Computer Programs using Optimized Obfuscations. CoRR, abs/2103.11882. https://arxiv.org/abs/2103.11882
  14. Karuna, P., Hemberg, E., O’Reilly, U.-M., & Rutar, N. (2021). Automating Cyber Threat Hunting Using NLP, Automated Query Generation, and Genetic Perturbation. CoRR, abs/2104.11576. https://arxiv.org/abs/2104.11576
  15. Emanuello, J., Ferguson-Walter, K., Hemberg, E., O’Reilly, U.-M., Ridley, A., Ross, D., Staheli, D., & Streilein, W. W. (2021). Proceedings - AI/ML for Cybersecurity: Challenges, Solutions, and Novel Ideas at SIAM Data Mining 2021. CoRR, abs/2104.13254. https://arxiv.org/abs/2104.13254
  16. Toutouh, J., Hemberg, E., & O’Reilly, U.-M. (2021). Fostering Diversity in Spatial Evolutionary Generative Adversarial Networks. CoRR, abs/2106.13590. https://arxiv.org/abs/2106.13590
  17. Hemberg, E., & O’Reilly, U.-M. (2021). Using a Collated Cybersecurity Dataset for Machine Learning and Artificial Intelligence. CoRR, abs/2108.02618. https://arxiv.org/abs/2108.02618
  18. Gunaratne, C., Reyes, R., Hemberg, E., & O’Reilly, U.-M. (2021). Evaluating Efficacy of Indoor Non-Pharmaceutical Interventions against COVID-19 Outbreaks with a Coupled Spatial-SIR Agent-Based Simulation Framework. CoRR, abs/2108.11025. https://arxiv.org/abs/2108.11025

2020

  1. O’Reilly, U.-M., Toutouh, J., Pertierra, M. A., Sanchez, D. P., Garcia, D., Lugo, A. E., Kelly, J., & Hemberg, E. (2020). Adversarial genetic programming for cyber security: a rising application domain where GP matters. Genet. Program. Evolvable Mach., 21(1-2), 219–250. https://doi.org/10.1007/S10710-020-09389-Y
  2. Verwer, S., Nadeem, A., Hammerschmidt, C. A., Bliek, L., Al-Dujaili, A., & O’Reilly, U.-M. (2020). The Robust Malware Detection Challenge and Greedy Random Accelerated Multi-Bit Search. In J. Ligatti & X. Ou (Eds.), AISec@CCS 2020: Proceedings of the 13th ACM Workshop on Artificial Intelligence and Security, Virtual Event, USA, 13 November 2020 (pp. 61–70). ACM. https://doi.org/10.1145/3411508.3421374
  3. Toutouh, J., Hemberg, E., & O’Reilly, U.-M. (2020). Re-purposing heterogeneous generative ensembles with evolutionary computation. In C. A. C. Coello (Ed.), GECCO ’20: Genetic and Evolutionary Computation Conference, Cancún Mexico, July 8-12, 2020 (pp. 425–434). ACM. https://doi.org/10.1145/3377930.3390229
  4. O’Reilly, U.-M., & Hemberg, E. (2020). Genetic programming: a tutorial introduction. In C. A. C. Coello (Ed.), GECCO ’20: Genetic and Evolutionary Computation Conference, Companion Volume, Cancún, Mexico, July 8-12, 2020 (pp. 512–525). ACM. https://doi.org/10.1145/3377929.3389882
  5. Shlapentokh-Rothman, M., Hemberg, E., & O’Reilly, U.-M. (2020). Securing the software defined perimeter with evolutionary co-optimization. In C. A. C. Coello (Ed.), GECCO ’20: Genetic and Evolutionary Computation Conference, Companion Volume, Cancún, Mexico, July 8-12, 2020 (pp. 1528–1536). ACM. https://doi.org/10.1145/3377929.3398085
  6. Al-Dujaili, A., & O’Reilly, U.-M. (2020). Sign Bits Are All You Need for Black-Box Attacks. 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. https://openreview.net/forum?id=SygW0TEFwH
  7. Liu, S., Lu, S., Chen, X., Feng, Y., Xu, K., Al-Dujaili, A., Hong, M., & O’Reilly, U.-M. (2020). Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks. Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, 119, 6282–6293. http://proceedings.mlr.press/v119/liu20j.html
  8. Pérez, E., Nesmachnow, S., Toutouh, J., Hemberg, E., & O’Reilly, U.-M. (2020). Parallel/distributed implementation of cellular training for generative adversarial neural networks. 2020 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2020, New Orleans, LA, USA, May 18-22, 2020, 512–518. https://doi.org/10.1109/IPDPSW50202.2020.00092
  9. Shen, H., Liang, L., Law, N., Hemberg, E., & O’Reilly, U.-M. (2020). Understanding Learner Behavior Through Learning Design Informed Learning Analytics. In D. A. Joyner, R. F. Kizilcec, & S. Singer (Eds.), L@S’20: Seventh ACM Conference on Learning @ Scale, Virtual Event, USA, August 12-14, 2020 (pp. 135–145). ACM. https://doi.org/10.1145/3386527.3405919
  10. Burd, H., Bell, A., Hemberg, E., & O’Reilly, U.-M. (2020). Analyzing Pre-Existing Knowledge and Performance in a Programming MOOC. In D. A. Joyner, R. F. Kizilcec, & S. Singer (Eds.), L@S’20: Seventh ACM Conference on Learning @ Scale, Virtual Event, USA, August 12-14, 2020 (pp. 281–284). ACM. https://doi.org/10.1145/3386527.3406728
  11. Gold, R., Hemberg, E., & O’Reilly, U.-M. (2020). Analyzing K-12 Blended MOOC Learning Behaviors. In D. A. Joyner, R. F. Kizilcec, & S. Singer (Eds.), L@S’20: Seventh ACM Conference on Learning @ Scale, Virtual Event, USA, August 12-14, 2020 (pp. 345–348). ACM. https://doi.org/10.1145/3386527.3406743
  12. Toutouh, J., Hemberg, E., & O’Reilly, U.-M. (2020). Analyzing the Components of Distributed Coevolutionary GAN Training. In T. Bäck, M. Preuss, A. H. Deutz, H. Wang, C. Doerr, M. T. M. Emmerich, & H. Trautmann (Eds.), Parallel Problem Solving from Nature - PPSN XVI - 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I (Vol. 12269, pp. 552–566). Springer. https://doi.org/10.1007/978-3-030-58112-1_38
  13. Hemberg, E., Zhang, L., & O’Reilly, U.-M. (2020). Exploring Adversarial Artificial Intelligence for Autonomous Adaptive Cyber Defense. In S. Jajodia, G. Cybenko, V. S. Subrahmanian, V. Swarup, C. Wang, & M. P. Wellman (Eds.), Adaptive Autonomous Secure Cyber Systems (pp. 41–61). Springer. https://doi.org/10.1007/978-3-030-33432-1_3
  14. Toutouh, J., Hemberg, E., & O’Reilly, U.-M. (2020). Data Dieting in GAN Training. In H. Iba & N. Noman (Eds.), Deep Neural Evolution - Deep Learning with Evolutionary Computation (pp. 379–400). Springer. https://doi.org/10.1007/978-981-15-3685-4_14
  15. Toutouh, J., Hemberg, E., & O’Reilly, U.-M. (2020). Re-purposing Heterogeneous Generative Ensembles with Evolutionary Computation. CoRR, abs/2003.13532. https://arxiv.org/abs/2003.13532
  16. Pérez, E., Nesmachnow, S., Toutouh, J., Hemberg, E., & O’Reilly, U.-M. (2020). Parallel/distributed implementation of cellular training for generative adversarial neural networks. CoRR, abs/2004.04633. https://arxiv.org/abs/2004.04633
  17. Toutouh, J., O’Reilly, U.-M., & Hemberg, E. (2020). Data Dieting in GAN Training. CoRR, abs/2004.04642. https://arxiv.org/abs/2004.04642
  18. O’Reilly, U.-M., Toutouh, J., Pertierra, M. A., Sanchez, D. P., Garcia, D., Lugo, A. E., Kelly, J., & Hemberg, E. (2020). Adversarial Genetic Programming for Cyber Security: A Rising Application Domain Where GP Matters. CoRR, abs/2004.04647. https://arxiv.org/abs/2004.04647
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