Taylor W. Killian

According to our database, Taylor W. Killian authored at least 16 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Offline Reinforcement Learning With Combinatorial Action Spaces.

Matthew Landers, Taylor W. Killian, Hugo Barnes, Thomas Hartvigsen, Afsaneh Doryab

CoRR, 2024

2023

Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning.

Taylor W. Killian, Sonali Parbhoo, Marzyeh Ghassemi

Trans. Mach. Learn. Res., 2023

Continuous Time Evidential Distributions for Irregular Time Series.

Taylor W. Killian, Haoran Zhang, Thomas Hartvigsen, Ava P. Amini

CoRR, 2023

2022

Machine Learning for Health (ML4H) 2022.

Antonio Parziale, Monica Agrawal, Shengpu Tang, Kristen Severson, Luis Oala, Adarsh Subbaswamy, Sayantan Kumar, Elora D. M. Schörverth, Stefan Hegselmann, Helen Zhou, Ghada Zamzmi, Purity Mugambi, Elena Sizikova, Girmaw Abebe Tadesse, Yuyin Zhou, Taylor W. Killian, Haoran Zhang, Fahad Kamran, Andrea Hobby, Mars Huang, Ahmed M. Alaa, Harvineet Singh, Irene Y. Chen, Shalmali Joshi

Proceedings of the Machine Learning for Health, 2022

Counterfactually Guided Policy Transfer in Clinical Settings.

Taylor W. Killian, Marzyeh Ghassemi, Shalmali Joshi

Proceedings of the Conference on Health, Inference, and Learning, 2022

2021

Medical Dead-ends and Learning to Identify High-Risk States and Treatments.

Mehdi Fatemi, Taylor W. Killian, Jayakumar Subramanian, Marzyeh Ghassemi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare.

Taylor W. Killian, Haoran Zhang, Jayakumar Subramanian, Mehdi Fatemi, Marzyeh Ghassemi

Proceedings of the Machine Learning for Health Workshop, 2020

Optimization Methods for Interpretable Differentiable Decision Trees Applied to Reinforcement Learning.

Andrew Silva, Matthew C. Gombolay, Taylor W. Killian, Ivan Dario Jimenez Jimenez, Sung-Hyun Son

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Multiple Sclerosis Severity Classification From Clinical Text.

Alister D. Costa, Stefan Denkovski, Michal Malyska, Sae Young Moon, Brandon Rufino, Zhen Yang, Taylor W. Killian, Marzyeh Ghassemi

Proceedings of the 3rd Clinical Natural Language Processing Workshop, 2020

2019

Kernelized Capsule Networks.

Taylor W. Killian, Justin A. Goodwin, Olivia M. Brown, Sung-Hyun Son

CoRR, 2019

Interpretable Reinforcement Learning via Differentiable Decision Trees.

Ivan Dario Jimenez Rodriguez, Taylor W. Killian, Sung-Hyun Son, Matthew C. Gombolay

CoRR, 2019

2018

Learning Robust Representations for Automatic Target Recognition.

Justin A. Goodwin, Olivia M. Brown, Taylor W. Killian, Sung-Hyun Son

CoRR, 2018

2017

Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes.

Taylor W. Killian, Samuel Daulton, Finale Doshi-Velez, George Dimitri Konidaris

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes.

Taylor W. Killian, George Dimitri Konidaris, Finale Doshi-Velez

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes.

Taylor W. Killian, George Dimitri Konidaris, Finale Doshi-Velez

CoRR, 2016

2013

Frequency uniqueness in ring oscillator Physical Unclonable Functions on FPGAs.

Muslim Mustapa, Mohammed Y. Niamat, Mansoor Alam, Taylor W. Killian

Proceedings of the IEEE 56th International Midwest Symposium on Circuits and Systems, 2013