Frequency uniqueness in ring oscillator Physical Unclonable Functions on FPGAs. - Muslim Mustapa; Mohammed Y. Niamat; Mansoor Alam; Taylor W. Killian (MWSCAS, 2013)

Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes. - Taylor W. Killian; George Dimitri Konidaris; Finale Doshi-Velez (CoRR, 2016)

Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes. - Taylor W. Killian; George Dimitri Konidaris; Finale Doshi-Velez (AAAI, 2017)

Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes. - Taylor W. Killian; Samuel Daulton; Finale Doshi-Velez; George Dimitri Konidaris (NIPS, 2017)

Learning Robust Representations for Automatic Target Recognition. - Justin A. Goodwin; Olivia M. Brown; Taylor W. Killian; Sung-Hyun Son (CoRR, 2018)

Interpretable Reinforcement Learning via Differentiable Decision Trees. - Ivan Dario Jimenez Rodriguez; Taylor W. Killian; Sung-Hyun Son; Matthew C. Gombolay (CoRR, 2019)

Kernelized Capsule Networks. - Taylor W. Killian; Justin A. Goodwin; Olivia M. Brown; Sung-Hyun Son (CoRR, 2019)

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 (ClinicalNLP@EMNLP, 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 (AISTATS, 2020)

An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare. - Taylor W. Killian; Haoran Zhang 003; Jayakumar Subramanian; Mehdi Fatemi; Marzyeh Ghassemi (ML4H@NeurIPS, 2020)

Medical Dead-ends and Learning to Identify High-Risk States and Treatments. - Mehdi Fatemi; Taylor W. Killian; Jayakumar Subramanian; Marzyeh Ghassemi (NeurIPS, 2021)

Counterfactually Guided Policy Transfer in Clinical Settings. - Taylor W. Killian; Marzyeh Ghassemi; Shalmali Joshi (CHIL, 2022)

Machine Learning for Health (ML4H) 2022. - Antonio Parziale; Monica Agrawal; Shengpu Tang; Kristen Severson 001; Luis Oala; Adarsh Subbaswamy; Sayantan Kumar; Elora D. M. Schörverth; Stefan Hegselmann; Helen Zhou; Ghada Zamzmi; Purity Mugambi; et al. (ML4H@NeurIPS, 2022)

Continuous Time Evidential Distributions for Irregular Time Series. - Taylor W. Killian; Haoran Zhang 003; Thomas Hartvigsen; Ava P. Amini (CoRR, 2023)

Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning. - Taylor W. Killian; Sonali Parbhoo; Marzyeh Ghassemi (Trans. Mach. Learn. Res., 2023)

Offline Reinforcement Learning With Combinatorial Action Spaces. - Matthew Landers; Taylor W. Killian; Hugo Barnes; Thomas Hartvigsen; Afsaneh Doryab (CoRR, 2024)

Identifying Differential Patient Care Through Inverse Intent Inference. - Hyewon Jeong; Siddharth Nayak; Taylor W. Killian; Sanjat Kanjilal (CoRR, 2024)

Clinically Motivated Sequential Decision Making Under Uncertainty in Offline Settings - Taylor W. Killian (2024)

SAINT: Attention-Based Modeling of Sub-Action Dependencies in Multi-Action Policies. - Matthew Landers; Taylor W. Killian; Thomas Hartvigsen; Afsaneh Doryab (CoRR, 2025)

Revisiting Reinforcement Learning for LLM Reasoning from A Cross-Domain Perspective. - Zhoujun Cheng; Shibo Hao; Tianyang Liu 003; Fan Zhou; Yutao Xie; Feng Yao; Yuexin Bian; Yonghao Zhuang 001; Nilabjo Dey; Yuheng Zha; Yi Gu; Kun Zhou; et al. (CoRR, 2025)