Finale Doshi
Finale Doshi
Assistant Professor, Harvard
Verified email at seas.harvard.edu
Title
Cited by
Cited by
Year
Towards a rigorous science of interpretable machine learning
F Doshi-Velez, B Kim
arXiv preprint arXiv:1702.08608, 2017
9942017
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
2952014
Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
AS Ross, F Doshi-Velez
arXiv preprint arXiv:1711.09404, 2017
2212017
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
2002014
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
1742017
Variational inference for the Indian buffet process
F Doshi, K Miller, J Van Gael, YW Teh
Artificial Intelligence and Statistics, 137-144, 2009
1632009
A Bayesian nonparametric approach to modeling motion patterns
J Joseph, F Doshi-Velez, AS Huang, N Roy
Autonomous Robots 31 (4), 383, 2011
1482011
A Bayesian nonparametric approach to modeling motion patterns
J Joseph, F Doshi-Velez, AS Huang, N Roy
Autonomous Robots 31 (4), 383, 2011
1482011
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
1432017
The variational Gaussian process
D Tran, R Ranganath, DM Blei
arXiv preprint arXiv:1511.06499, 2015
1252015
The infinite partially observable Markov decision process
F Doshi-Velez
Advances in neural information processing systems, 477-485, 2009
1162009
The infinite partially observable Markov decision process
F Doshi-Velez
Advances in neural information processing systems, 477-485, 2009
1162009
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
1152017
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
1062016
Beyond sparsity: Tree regularization of deep models for interpretability
M Wu, MC Hughes, S Parbhoo, M Zazzi, V Roth, F Doshi-Velez
arXiv preprint arXiv:1711.06178, 2017
1002017
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
902018
Guidelines for reinforcement learning in healthcare
O Gottesman, F Johansson, M Komorowski, A Faisal, D Sontag, ...
Nat Med 25 (1), 16-18, 2019
872019
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
862019
A roadmap for a rigorous science of interpretability
F Doshi-Velez, B Kim
arXiv preprint arXiv:1702.08608 2, 2017
852017
Reinforcement learning with limited reinforcement: Using Bayes risk for active learning in POMDPs
F Doshi, J Pineau, N Roy
Proceedings of the 25th international conference on Machine learning, 256-263, 2008
842008
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Articles 1–20