Supervised Learning
In supervised learning the desired outputs are known for each input, and the task is to learn a mapping between them that generalizes well to new inputs.
People
     [Expand to show all 15][Minimize]
Garrett Bingham Ph.D. Alumni bingham [at] cs utexas edu
Eliana Feasley Formerly affiliated Ph.D. Student elie [at] cs utexas edu
Peggy Fidelman Formerly affiliated Ph.D. Student peggyf [at] cs utexas edu
Olivier Francon Collaborator olivier francon [at] cognizant com
Kim Houck Ph.D. Alumni houck [at] cs utexas edu
Alan J. Lockett Ph.D. Alumni alan lockett [at] gmail com
Marlan McInnes-Taylor Masters Student marlan [at] cs utexas edu
Risto Miikkulainen Faculty risto [at] cs utexas edu
Arjun Nagineni Undergraduate Alumni arjun nagineni [at] utexas edu
Xin Qiu Collaborator xin qiu [at] cognizant com
Vito Ruiz Masters Alumni
Jake Ryan Undergraduate Alumni
Kenneth Stanley Postdoctoral Alumni kstanley [at] cs ucf edu
Wesley Tansey Formerly affiliated Collaborator tansey [at] cs utexas edu
Austin Waters Ph.D. Alumni austin [at] cs utexas edu
Publications
     [Expand to show all 35][Minimize]
NeuroComb: Improving SAT Solving with Graph Neural Networks 2024
Wenxi Wang, Yang Hu, Mohit Tiwari, Sarfraz Khurshid, Kenneth McMillan, Risto Miikkulainen, In Proceedings of the International Conference on Learning Representations, 2024. (also arXiv:2110.14053).
Semantic Density: Uncertainty Quantification in Semantic Space for Large Language Models 2024
Xin Qiu, Risto Miikkulainen, arXiv:2405.13845 (2024).
AutoInit: Analytic Signal-Preserving Weight Initialization for Neural Networks 2023
Garrett Bingham and Risto Miikkulainen, In Proceedings of the 37th AAAI Conference on Artificial Intelligence, 2023. (also arXiv:2021.08958).
Efficient Activation Function Optimization through Surrogate Modeling 2023
Garrett Bingham and Risto Miikkulainen, In Proceedings of the 23rd Conference on Neural Information Processing Systems (NeurIPS 2023), 2023.
Evolutionary Supervised Machine Learning 2023
Risto Miikkulainen, In Handbook of Evolutionary Machine Learning, W. Banzhaf, P. Machado, and M. Zhang (Eds.), New York, 2023. Springer.
Pandemic Resilience: Developing an AI-calibrated Ensemble of Models to Inform Decision Making 2023
GPAI, Technical Report, Global Partnership on Artificial Intelligence.
Detecting Misclassification Errors in Neural Networks with a Gaussian Process Model 2022
Xin Qiu and Risto Miikkulainen, In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI-2022), 2022. (Also arXiv:2010.02065, which also includes the appendices).
Discovering Parametric Activation Functions 2022
Garrett Bingham and Risto Miikkulainen, Neural Networks, Vol. 148 (2022), pp. 48-65.
Effective Regularization Through Loss-Function Metalearning 2021
Santiago Gonzalez and Risto Miikkulainen, arXiv:2010.00788 (2021).
Improving Neural Network Learning Through Dual Variable Learning Rates 2021
Elizabeth Liner, Risto Miikkulainen, In Proceedings of the International Joint Conference on Neural Networks, 2021.
Optimizing Loss Functions Through Multivariate Taylor Polynomial Parameterization 2021
Santiago Gonzalez and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 305-313, 2021.
Regularized Evolutionary Population-Based Training 2021
Jason Liang, Santiago Gonzalez, Hormoz Shahrzad, and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 323-331, 2021.
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings 2021
Elliot Meyerson and Risto Miikkulainen, To Appear In International Conference on Learning Representations, 2021.
Evolutionary Optimization of Deep Learning Activation Functions 2020
Garrett Bingham, William Macke, and Risto Miikkulainen, In Genetic and Evolutionary Computation Conference (GECCO '20), pp. 289-296, Cancun, Mexico, 2020.
From Nodes to Networks: Evolving Recurrent Neural Networks 2020
Aditya Rawal, Risto Miikkulainen, In Deep Neural Evolution: Deep Learning with Evolutionary Computation, H. Iba and N. Noman (Eds.), pp. 233-251 2020. Springer. (also arxiv:1803.04439).
Improved Training Speed, Accuracy, and Data Utilization Through Loss Function Optimization 2020
Santiago Gonzalez and Risto Miikkulainen, In Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1-8, July 2020.
Improving Deep Learning Through Loss-Function Evolution 2020
Santiago Gonzalez, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel 2020
Xin Qiu, Elliot Meyerson, Risto Miikkulainen, In International Conference on Learning Representations, 2020.
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings 2020
Elliot Meyerson and Risto Miikkulainen, arxiv:2010.02354 (2020).
Evolving Deep Neural Networks 2019
Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Dan Fink, Olivier Francon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy, and Babak Hodjat, In Artificial Intelligence in the Age of Neural Networks and Brain Computing, Robert Kozma, Cesare Alippi, Yoonsuck Choe, and Francesco Carlo Morabito (Eds.), pp. 293-312 2019. Amsterdam: Elsev...
Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering 2018
Elliot Meyerson and Risto Miikkulainen, In Proceedings of the Sixth International Conference on Learning Representations (ICLR), Vancouver, Canada 2018.
Discovering Gated Recurrent Neural Network Architectures 2018
Aditya Rawal, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Learning Useful Features For Poker 2018
Arjun Nagineni, Technical Report, Department of Computer Sciences, The University of Texas at Austin.
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Back 2018
Elliot Meyerson, Risto Miikkulainen, In Proceedings of the 35th International Conference on Machine Learning, pp. 739-748 2018.
Efficient Sampling for Design Optimization of an SLS Product 2017
Nancy Xu, Cem C. Tutum, In Proceedings of the 28th Annual International Solid Freeform Fabrication Symposium, pp. 12, Austin, TX, August 2017.
Surrogate-based Evolutionary Optimization for Friction Stir Welding 2016
Cem C Tutum, Shaayaan Sayed and Risto Miikkulainen, In Proceedings of IEEE World Congress on Computational Intelligence (WCCI 2016), pp. 8 pages, Vancouver, BC, Canada, July 2016.
GRADE: Machine Learning Support for Graduate Admissions 2014
Austin Waters, Risto Miikkulainen, AI Magazine, Vol. 35 (2014), pp. 64-75.
Infinite-Word Topic Models for Digital Media 2014
Austin Waters, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
GRADE: Machine Learning Support for Graduate Admissions 2013
Austin Waters, Risto Miikkulainen, In Proceedings of the 25th Conference on Innovative Applications of Artificial Intelligence 2013.
Accelerating Evolution via Egalitarian Social Learning 2012
Wesley Tansey, Eliana Feasley, and Risto Miikkulainen, In Proceedings of the 14th Annual Genetic and Evolutionary Computation Conference (GECCO 2012), Philadelphia, Pennsylvania, USA 2012.
Temporal Convolution Machines for Sequence Learning 2009
Alan J Lockett and Risto Miikkulainen, Technical Report AI-09-04, Department of Computer Sciences, the University of Texas at Austin.
Detecting Motion in the Environment with a Moving Quadruped Robot 2007
Peggy Fidelman, Thayne Coffman and Risto Miikkulainen, In RoboCup-2006: Robot Soccer World Cup X, Gerhard Lakemeyer and Elizabeth Sklar and Domenico Sorenti and Tomoichi Takahashi (Eds.), pp. 219-231, Berlin 2007. Springer Verlag.
Learning Concept Drift with a Committee of Decision Trees 2003
Kenneth O. Stanley, Technical Report AI03-302, Department of Computer Sciences, The University of Texas at Austin.
Parsing Embedded Clauses with Distributed Neural Networks 1994
Risto Miikkulainen and Dennis Bijwaard, In Proceedings of the Twelfth National Conference on Artificial Intelligence, pp. 858-864, January 1994.
Controlling Search for the Consequences of New Information during Knowledge Integration 1989
K. Murray and Bruce Porter , In Proceedings of the Sixth International Workshop on Machine Learning, pp. 290-295, Ithaca, NY, June 1989.
Projects
Teaching an Agent Manually via Evaluative Reinforcement (TAMER) 2008 - Present
Intrusion Detection 1998 - 1998
Data Rectification for Process Control 1992 - 1992
Software/Data
ESL This is the C# source code for the experiments with Egalitarian Social Learning (ESL) in a robot foraging domain. The re... 2012
Demos
Egalitarian Social Learning (ESL) in Robot ForagingWesley Tansey2012
Labs
Neural Networks Knowledge Representation & Reasoning