Evolutionary Architecture Search for Deep Multitask Networks (2018)
Citation: Liang, J., Meyerson, E., and Miikkulainen, R. (in press). Evolutionary Architecture Search for Deep Multitask Networks. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2018, Kyoto, Japan).
Abstract & Download
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing—and Back (2018)
Citation: Meyerson, E. and Miikkulainen, R. (2018). Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing—and Back. In Proceedings of the International Conference on Machine Learning (ICML-2018, Stockholm, Sweden).
Abstract & Download
Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering (2018)
Citation: Meyerson, E. and Miikkulainen, R. (2018). In Proceedings of the Sixth International Conference on Learning Representations (ICLR), Vancouver, Canada, 2018.
Abstract & Download
From Nodes to Networks: Evolving Recurrent Neural Networks (2018)
Citation: Rawal, A. and Miikkulainen, R. (2018). From Nodes to Networks: Evolving Recurrent Neural Networks. arXiv:1803.04439
Abstract & Download
Sentient Ascend: AI-Based Massively Multivariate Conversion Rate Optimization (2018)
Citation: R. Miikkulainen, N. Iscoe, A. Shagrin, R. Rapp, S. Nazari, P. McGrath, C. Schoolland, E. Achkar, M. Brundage, J. Miller, J. Epstein, G. Lamba (2018). Sentient Ascend: AI-Based Massively Multivariate Conversion Rate Optimization. In Proceedings of the Thirtieth Conference on Innovative Applications of Artificial Intelligence (AAAI / IAAI-2018, New Orleans, LA; IAAI-2018 Deployed Application Award; GECCO-2017 Human-Competitive Results Bronze Award).
Abstract & Download
Enhancing Evolutionary Optimization in Uncertain Environments via Multi-Armed Bandit Algorithms (2018)
Citation: Qiu, X. and Miikkulainen, R. (2018). Enhancing Evolutionary Optimization in Uncertain Environments via Multi-Armed Bandit Algorithms. arXiv:1803.03737
Abstract & Download
Enhanced Optimization with Composite Objectives and Novelty Selection (2018)
Citation: Shahrzad, H., Fink, D., and Miikkulainen, R. (2018). Enhanced Optimization with Composite Objectives and Novelty Selection. In Proceedings of the 2018 Conference on Artificial Life (ALife’2018, Tokyo, Japan).
Abstract & Download
How to Select a Winner in Evolutionary Optimization? (2017)
Citation: Miikkulainen, R., Shahrzad, H., Duffy, N., and Long, P. (2017). How to select a winner in evolutionary optimization? In Proceedings of the IEEE Symposium Series in Computational Intelligence (IEEE-SSCI 2017, Honolulu, HI).
Abstract & Download
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).
Abstract & Download
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).
Latent Geometry and Memorization in Generative Models (2017)
Citation: Feiszli, M. (2017). Latent Geometry and Memorization in Generative Models. arXiv:1705.09303.
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.
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.
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.
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).
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).
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).
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).
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.
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.
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.
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.
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.
Abstract & Download
EC-Star: 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.
Abstract & Download
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.
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.