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Finale Doshi-Velez
Finale Doshi-Velez
Professor, Harvard
在 seas.harvard.edu 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
Towards a rigorous science of interpretable machine learning
F Doshi-Velez, B Kim
arXiv preprint arXiv:1702.08608, 2017
23632017
Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
A Ross, F Doshi-Velez
Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018
4512018
Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis
F Doshi-Velez, Y Ge, I Kohane
Pediatrics 133 (1), e54-e63, 2014
4032014
Right for the right reasons: Training differentiable models by constraining their explanations
AS Ross, MC Hughes, F Doshi-Velez
arXiv preprint arXiv:1703.03717, 2017
3832017
Do no harm: a roadmap for responsible machine learning for health care
J Wiens, S Saria, M Sendak, M Ghassemi, VX Liu, F Doshi-Velez, K Jung, ...
Nature medicine 25 (9), 1337-1340, 2019
3352019
Accountability of AI under the law: The role of explanation
F Doshi-Velez, M Kortz, R Budish, C Bavitz, S Gershman, D O'Brien, ...
arXiv preprint arXiv:1711.01134, 2017
3342017
Guidelines for reinforcement learning in healthcare
O Gottesman, F Johansson, M Komorowski, A Faisal, D Sontag, ...
Nature medicine 25 (1), 16-18, 2019
2452019
Unfolding physiological state: Mortality modelling in intensive care units
M Ghassemi, T Naumann, F Doshi-Velez, N Brimmer, R Joshi, ...
Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014
2452014
A bayesian framework for learning rule sets for interpretable classification
T Wang, C Rudin, F Doshi-Velez, Y Liu, E Klampfl, P MacNeille
The Journal of Machine Learning Research 18 (1), 2357-2393, 2017
2002017
Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning
S Depeweg, JM Hernandez-Lobato, F Doshi-Velez, S Udluft
International Conference on Machine Learning, 1184-1193, 2018
1912018
Beyond sparsity: Tree regularization of deep models for interpretability
M Wu, M Hughes, S Parbhoo, M Zazzi, V Roth, F Doshi-Velez
Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018
1902018
Variational inference for the Indian buffet process
F Doshi, K Miller, J Van Gael, YW Teh
Artificial Intelligence and Statistics, 137-144, 2009
1752009
A Bayesian nonparametric approach to modeling motion patterns
J Joseph, F Doshi-Velez, AS Huang, N Roy
Autonomous Robots 31 (4), 383-400, 2011
1732011
A Bayesian nonparametric approach to modeling motion patterns
J Joseph, F Doshi-Velez, AS Huang, N Roy
Autonomous Robots 31 (4), 383-400, 2011
1732011
How do humans understand explanations from machine learning systems? an evaluation of the human-interpretability of explanation
M Narayanan, E Chen, J He, B Kim, S Gershman, F Doshi-Velez
arXiv preprint arXiv:1802.00682, 2018
1592018
Learning and policy search in stochastic dynamical systems with bayesian neural networks
S Depeweg, JM Hernández-Lobato, F Doshi-Velez, S Udluft
arXiv preprint arXiv:1605.07127, 2016
1442016
The infinite partially observable Markov decision process
F Doshi-Velez
Advances in neural information processing systems 22, 2009
1442009
The infinite partially observable Markov decision process
F Doshi-Velez
Advances in neural information processing systems 22, 2009
1442009
An evaluation of the human-interpretability of explanation
I Lage, E Chen, J He, M Narayanan, B Kim, S Gershman, F Doshi-Velez
arXiv preprint arXiv:1902.00006, 2019
1262019
The myth of generalisability in clinical research and machine learning in health care
J Futoma, M Simons, T Panch, F Doshi-Velez, LA Celi
The Lancet Digital Health 2 (9), e489-e492, 2020
1122020
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