| Women, politics and Twitter: using machine learning to change the discourse L Cuthbertson, A Kearney, R Dawson, A Zawaduk, E Cuthbertson, ... arXiv preprint arXiv:1911.11025, 2019 | 17 | 2019 |
| Tidbd: Adapting temporal-difference step-sizes through stochastic meta-descent A Kearney, V Veeriah, JB Travnik, RS Sutton, PM Pilarski arXiv preprint arXiv:1804.03334, 2018 | 17 | 2018 |
| Temporal-difference learning to assist human decision making during the control of an artificial limb AL Edwards, A Kearney, MR Dawson, RS Sutton, PM Pilarski arXiv preprint arXiv:1309.4714, 2013 | 14 | 2013 |
| Predictions, Surprise, and Predictions of Surprise in General Value Function Architectures PMP J. Gunther, A. Kearney, M. R. Dawson, C. Sherstan AAAI 2018 Fall Symposium on Reasoning and Learning in Real-World Systems for …, 2018 | 12 | 2018 |
| Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures J Günther, NM Ady, MR Kearney, Alex, Dawson, PM Pilarski arXiv preprint arXiv:1908.05751, 2019 | 11 | 2019 |
| Learning feature relevance through step size adaptation in temporal-difference learning A Kearney, V Veeriah, J Travnik, PM Pilarski, RS Sutton arXiv preprint arXiv:1903.03252, 2019 | 11 | 2019 |
| Meta-learning for Predictive Knowledge Architectures: A Case Study Using TIDBD on a Sensor-rich Robotic Arm. J Günther, A Kearney, NM Ady, MR Dawson, PM Pilarski AAMAS, 1967-1969, 2019 | 5 | 2019 |
| What’sa good prediction? Challenges in evaluating an agent’s knowledge A Kearney, AJ Koop, PM Pilarski Adaptive Behavior 31 (3), 197-212, 2023 | 3 | 2023 |
| What Should I Know? Using Meta-Gradient Descent for Predictive Feature Discovery in a Single Stream of Experience A Kearney, A Koop, J Günther, PM Pilarski Conference on Lifelong Learning Agents, 604-616, 2022 | 2 | 2022 |
| Finding useful predictions by meta-gradient descent to improve decision-making A Kearney, A Koop, J Günther, PM Pilarski arXiv preprint arXiv:2111.11212, 2021 | 2 | 2021 |
| Prediction, Knowledge, and Explainability: Examining the Use of General Value Functions in Machine Knowledge A Kearney, J Günther, PM Pilarski Frontiers in Artificial Intelligence 5, 826724, 2022 | 1 | 2022 |
| What's a good prediction? Issues in evaluating general value functions through error A Kearney, A Koop, PM Pilarski arXiv [Preprint]. arXiv: 2001.08823, 2020 | 1 | 2020 |
| When is a prediction knowledge? A Kearney, PM Pilarski arXiv preprint arXiv:1904.09024, 2019 | 1 | 2019 |
| Making Meaning: Semiotics Within Predictive Knowledge Architectures A Kearney, O Oxton arXiv preprint arXiv:1904.09023, 2019 | 1 | 2019 |
| Evaluating Predictive Knowledge A Kearney, A Koop, C Sherstan, J Günther, RS Sutton, PM Pilarski, ... AAAI 2018 Fall Symposium on Reasoning and Learning in Real-World Systems for …, 2018 | 1 | 2018 |
| Device and method for machine-learning step-size adaptation AK Kearney US Patent 11,625,598, 2023 | | 2023 |
| Letting the Agent Take the Wheel: Principles for Constructive and Predictive Knowledge AK Kearney | | 2023 |
| Dealing With Changing Contexts In Myoelectric Control AJ Koop, A Kearney, M Bowling, PM Pilarski Myoelectric Controls Symposium, Fredericton, New Brunswick, 2014 | | 2014 |
Patrick M. PilarskiProfessor, University of Alberta, Amii (Alberta Machine Intelligence Institute)Verified email at ualberta.ca
