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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 14]
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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
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 34]
[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).
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 Foraging
Wesley Tansey
2012
Labs
Neural Networks
Knowledge Representation & Reasoning