Taylor W. Killian

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Dr. Marzyeh GhassemiAssociate Professor, EECS/IMES, MITDirección de correo verificada de mit.edu
Matthew GombolayAssociate Professor, Georgia Institute of TechnologyDirección de correo verificada de cc.gatech.edu
Finale Doshi-VelezProfessor, HarvardDirección de correo verificada de seas.harvard.edu
George KonidarisBrownDirección de correo verificada de cs.brown.edu
Andrew SilvaResearch Scientist, Toyota Research InstituteDirección de correo verificada de tri.global
Mehdi FatemiWand.aiDirección de correo verificada de wand.ai
Haoran ZhangPhD Student, Massachusetts Institute of TechnologyDirección de correo verificada de mit.edu
Samuel DaultonResearch Scientist, MetaDirección de correo verificada de meta.com
Tadd TruscottAssociate Professor, KAUST (King Abdullah University of Science and Technology)Dirección de correo verificada de kaust.edu.sa
Justin GoodwinMIT Lincoln LaboratoryDirección de correo verificada de ll.mit.edu
Shalmali JoshiColumbia UniversityDirección de correo verificada de cumc.columbia.edu
Jiayu YaoPostdoctoral Fellow, Columbia University DSIDirección de correo verificada de columbia.edu
Dylan Hadfield-MenellMassachusetts Institute of TechnologyDirección de correo verificada de csail.mit.edu
Stephen CasperPhD student, MITDirección de correo verificada de mit.edu
Sonali ParbhooAssistant Professor, Imperial College LondonDirección de correo verificada de imperial.ac.uk
Tom HartvigsenAssistant Professor, University of VirginiaDirección de correo verificada de virginia.edu
Vinith M. SuriyakumarMassachusetts Institute of TechnologyDirección de correo verificada de mit.edu
Ava P. AminiMicrosoft ResearchDirección de correo verificada de microsoft.com
Neha HulkundMassachusetts Institute of TechnologyDirección de correo verificada de mit.edu
Afsaneh DoryabAssistant Professor, University of VirginiaDirección de correo verificada de virginia.edu

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Taylor W. Killian

Graduate Researcher, University of Toronto

Dirección de correo verificada de cs.toronto.edu - Página principal

Machine Learning Reinforcement Learning Healthcare Transfer Learning Causal Inference


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Optimization methods for interpretable differentiable decision trees applied to reinforcement learning A Silva, M Gombolay, T Killian, I Jimenez, SH Son International conference on artificial intelligence and statistics, 1855-1865, 2020	180*	2020
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes TW Killian, S Daulton, F Doshi-Velez, G Konidaris Advances in Neural Information Processing Systems, 6251-6262, 2017	132	2017
Medical dead-ends and learning to identify high-risk states and treatments M Fatemi, TW Killian, J Subramanian, M Ghassemi Advances in Neural Information Processing Systems 34, 4856-4870, 2021	52	2021
An empirical study of representation learning for reinforcement learning in healthcare TW Killian, H Zhang, J Subramanian, M Fatemi, M Ghassemi Proceedings of the Machine Learning for Health NeurIPS Workshop 136, 139-160, 2020	48	2020
Direct Policy Transfer with Hidden Parameter Markov Decision Processes J Yao, TW Killian, G Konidaris, F Doshi-Velez Lifelong Learning: A Reinforcement Learning Approach Workshop at FAIM 2018, 2018	32*	2018
Rebound and jet formation of a fluid-filled sphere TW Killian, RA Klaus, TT Truscott Physics of Fluids 24 (12), 2012	13	2012
Counterfactually guided policy transfer in clinical settings TW Killian, M Ghassemi, S Joshi Conference on Health, Inference, and Learning, 5-31, 2022	12	2022
Multiple sclerosis severity classification from clinical text AD Costa, S Denkovski, M Malyska, SY Moon, B Rufino, Z Yang, T Killian, ... arXiv preprint arXiv:2010.15316, 2020	12	2020
Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes T Killian, G Konidaris, F Doshi-Velez arXiv preprint arXiv:1612.00475, 2016	12	2016
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning TW Killian, S Parbhoo, M Ghassemi Transactions on Machine Learning Research, 2023	10	2023
White-box adversarial policies in deep reinforcement learning S Casper, T Killian, G Kreiman, D Hadfield-Menell arXiv preprint arXiv:2209.02167, 2022	7	2022
Kernelized capsule networks T Killian, J Goodwin, O Brown, SH Son arXiv preprint arXiv:1906.03164, 2019	6	2019
Machine learning for health (ml4h) 2022 A Parziale, M Agrawal, S Tang, K Severson, L Oala, A Subbaswamy, ... Machine Learning for Health, 1-11, 2022	3	2022
Systems and methods for detection of concealed threats MA Deangelus, J Goodwin, N Kaushik, A Opipari, T Killian US Patent App. 17/219,837, 2022	1	2022
Red teaming with mind reading: White-box adversarial policies against rl agents S Casper, T Killian, G Kreiman, D Hadfield-Menell arXiv preprint arXiv:2209.02167, 2022	1	2022
Self healing: solid spheres impacting soap bubbles T Killian, J Bryson, J Huey, JC Bird, JC Nave, T Truscott APS Division of Fluid Dynamics Meeting Abstracts, R4. 005, 2012	1*	2012
Robust Autonomy Emerges from Self-Play M Cusumano-Towner, D Hafner, A Hertzberg, B Huval, A Petrenko, ... arXiv preprint arXiv:2502.03349, 2025		2025
Identifying Differential Patient Care Through Inverse Intent Inference H Jeong, S Nayak, T Killian, S Kanjilal, M Ghassemi arXiv preprint arXiv:2411.07372, 2024		2024
Offline Reinforcement Learning With Combinatorial Action Spaces M Landers, TW Killian, H Barnes, T Hartvigsen, A Doryab arXiv preprint arXiv:2410.21151, 2024		2024
Clinically Motivated Sequential Decision Making Under Uncertainty in Offline Settings TW Killian University of Toronto (Canada), 2024		2024

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