Conversion Rate Optimization through Evolutionary Computation (2017)

Citation: Miikkulainen, R., Iscoe, N., Shagrin, A., Cordell, R., Nazari, S., Schoolland, C., Brundage, M., Epstein, J., Dean, R. and Lamba, G. (2017). Conversion Rate Optimization through Evolutionary Computation. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2017, Berlin, Germany).
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Discovering Evolutionary Stepping Stones through Behavior Domination (2017)

Citation: Meyerson, E. and Miikkulainen, R. (2017). Discovering Evolutionary Stepping Stones through Behavior Domination. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2017, Berlin, Germany).

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Latent Geometry and Memorization in Generative Models (2017)

Citation: Feiszli, M. (2017). Latent Geometry and Memorization in Generative Models. arXiv:1705.09303.

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Evolving Deep Neural Networks (2017)

Citation: Miikkulainen, R., Meyerson, E., Liang, J., Rawal, A., Shahrzad, H., Fink, D. Francon, O., Raju, B., Navruzyan, A., Hodjat, B., and Duffy, N. (2017). Evolving Deep Neural Networks. arXiv:1703.00548.

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The power of localization for efficiently learning linear separators with noise (2017)

Citation: Awasthi, P., Balcan M. F., and Long, P. M (2017). The power of localization for efficiently learning linear separators with noise. arXiv:1307.8371.

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PRETSL: Distributed probabilistic rule evolution for time-series classification (2016)

Citation: Hodjat, B., Shahrzad, H., Miikkulainen, R., Murray, L., and Holmes, C. (in press). PRETSL: Distributed probabilistic rule evolution for time-series classification. In Genetic Programming Theory and Practice XIV. Springer, New York.

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Visual Product Discovery (2016)

Citation: Legrand, D., Long, P. M., Brundage, M., Angelopoulos, T., Francon, O., Garg, V., Mann, W., Ramamurthy, V., Saliou, A., Simmons, B., Skipper, P., Tsatsin, P., Vistnes, R., Duffy, N. (2016). Visual Product Discovery. In the “Machine Learning Meets Fashion” Workshop at the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (San Francisco, CA).

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Distributed age-layered novelty search (2016)

Citation: Hodjat, B., Shahrzad, H., and Miikkulainen, R. (2016). Distributed age-layered novelty search. In Proceedings of the Fifteenth International Conference on the Synthesis and Simulation of Living Systems (Alife’16, Cancun, Mexico).

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Estimating the advantage of age-layering in evolutionary algorithms (2016)

Citation: Shahrzad, H., Hodjat, B., and Miikkulainen, R. (2016). Estimating the advantage of age-layering in evolutionary algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2016, Denver, CO).

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L-SR1: A Novel Second Order Optimization Method for Deep Learning (2016)

Citation: Ramamurthy, V. and Duffy, N. (2016). L-SR1: A Novel Second Order Optimization Method for Deep Learning. (+ supplement). In NIPS 2016 Workshop on Nonconvex Optimization for Machine Learning: Theory and Practice, at the Neural Information Processing Systems Conference (NIPS 2016, Barcelona, Spain).

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Massively distributed simultaneous evolution and cross-validation in EC-star (2016)

Citation: Hodjat, B. and Shahrzad, J. (2016). nPool: Massively distributed simultaneous evolution and cross-validation in EC-star. In Riolo, R., Worzel, W. P., Kotanchek, M., and Kordon, A. editors, Genetic Programming Theory and Practice XIII, Genetic and Evolutionary Computation. Springer, New York.

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Surprising properties of dropout in deep networks (2016)

Citation: Helmbold, D. P. and Long, P. M. (2016). Surprising properties of dropout in deep networks. arXiv:1602.04484.

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Tackling the Boolean multiplexer function using a highly distributed genetic programming system (2015)

Citation: Shahrzad, H. and Hodjat, B. (2015). Tackling the Boolean multiplexer function using a highly distributed genetic programming system. In Riolo, R., Worzel, W. P., and Kotanchek, M., editors,Genetic Programming Theory and Practice XII, pages 167-179. Springer, New York.

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Maintenance of a long running distributed genetic programming system for solving problems requiring big data (2014)

Citation: Hodjat, B., Hemberg, E., Shahrzad, H., and O’Reilly, U.-M. (2014). Maintenance of a long running distributed genetic programming system for solving problems requiring big data. In Genetic Programming Theory and Practice XI, pages 65-83. Springer, New York.

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Learning Decision Lists with Lagged Physiological Time Series (2014)

Citation: Hemberg, E., Veeramachaneni, K., Wanigasekara, P., Shahrzad, H., Hodjat, B., and O’Reilly, U.-M. (2014). Learning Decision Lists with Lagged Physiological Time Series. In Workshop on Data Mining for Medicine and Healthcare at the 14th SIAM International Conference on Data Mining, pages 82-87.

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Introducing an age-varying fitness estimation function (2013)

Citation: Hodjat, B. and Shahrzad, H. (2013). Introducing an age-varying fitness estimation function. In Riolo, R., Vladislavleva, E., Ritchie, M. D., and Moore, J. H., editors, Genetic Programming Theory and Practice X, pages 59-71. Springer, New York.

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A massive-scale, hub and spoke, distributed genetic programming system (2013)

Citation: O’Reilly, U.-M., Wagy, M., and Hodjat, B. (2013). EC-Star: A massive-scale, hub and spoke, distributed genetic programming system. In Riolo, R., Vladislavleva, E., Ritchie, M. D., and Moore, J. H., editors, Genetic Programming Theory and Practice X, pages 73-85. Springer, New York.

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