Finale Doshi
Finale Doshi
Assistant Professor, Harvard
Verifierad e-postadress på seas.harvard.edu
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Towards a rigorous science of interpretable machine learning
F Doshi-Velez, B Kim
arXiv preprint arXiv:1702.08608, 2017
7962017
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
2712014
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
1832014
Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
AS Ross, F Doshi-Velez
Thirty-second AAAI conference on artificial intelligence, 2018
1772018
Variational inference for the Indian buffet process
F Doshi, K Miller, J Van Gael, YW Teh
Artificial Intelligence and Statistics, 137-144, 2009
1602009
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
1492017
A Bayesian nonparametric approach to modeling motion patterns
J Joseph, F Doshi-Velez, AS Huang, N Roy
Autonomous Robots 31 (4), 383, 2011
1422011
A Bayesian nonparametric approach to modeling motion patterns
J Joseph, F Doshi-Velez, AS Huang, N Roy
Autonomous Robots 31 (4), 383, 2011
1422011
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
1242017
The variational Gaussian process
D Tran, R Ranganath, DM Blei
arXiv preprint arXiv:1511.06499, 2015
1162015
The infinite partially observable Markov decision process
F Doshi-Velez
Advances in neural information processing systems, 477-485, 2009
1112009
The infinite partially observable Markov decision process
F Doshi-Velez
Advances in neural information processing systems, 477-485, 2009
1112009
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
1002017
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
972016
Beyond sparsity: Tree regularization of deep models for interpretability
M Wu, MC Hughes, S Parbhoo, M Zazzi, V Roth, F Doshi-Velez
Thirty-Second AAAI Conference on Artificial Intelligence, 2018
852018
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
812018
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
812008
Efficient model learning for dialog management
F Doshi, N Roy
Proceedings of the ACM/IEEE international conference on Human-robot …, 2007
782007
A roadmap for a rigorous science of interpretability
F Doshi-Velez, B Kim
arXiv preprint arXiv:1702.08608 2, 2017
772017
Spoken language interaction with model uncertainty: an adaptive human–robot interaction system
F Doshi, N Roy
Connection Science 20 (4), 299-318, 2008
762008
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Artiklar 1–20