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Isaac Lage
Isaac Lage
Verified email at g.harvard.edu - Homepage
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Cited by
Cited by
Year
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
2102019
Human-in-the-loop interpretability prior
I Lage, A Ross, SJ Gershman, B Kim, F Doshi-Velez
Advances in neural information processing systems 31, 2018
1572018
Human-in-the-loop interpretability prior
I Lage, A Ross, SJ Gershman, B Kim, F Doshi-Velez
Advances in neural information processing systems 31, 2018
1572018
Evaluating reinforcement learning algorithms in observational health settings
O Gottesman, F Johansson, J Meier, J Dent, D Lee, S Srinivasan, L Zhang, ...
arXiv preprint arXiv:1805.12298, 2018
1192018
Human evaluation of models built for interpretability
I Lage, E Chen, J He, M Narayanan, B Kim, SJ Gershman, F Doshi-Velez
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 7 …, 2019
1182019
Exploring computational user models for agent policy summarization
I Lage, D Lifschitz, F Doshi-Velez, O Amir
IJCAI: proceedings of the conference 28, 1401, 2019
762019
Promises and pitfalls of black-box concept learning models
A Mahinpei, J Clark, I Lage, F Doshi-Velez, W Pan
arXiv preprint arXiv:2106.13314, 2021
602021
The neural lasso: Local linear sparsity for interpretable explanations
A Ross, I Lage, F Doshi-Velez
Workshop on Transparent and Interpretable Machine Learning in Safety …, 2017
242017
Learning interpretable concept-based models with human feedback
I Lage, F Doshi-Velez
arXiv preprint arXiv:2012.02898, 2020
182020
When does uncertainty matter?: Understanding the impact of predictive uncertainty in ML assisted decision making
S McGrath, P Mehta, A Zytek, I Lage, H Lakkaraju
arXiv preprint arXiv:2011.06167, 2020
152020
Toward robust policy summarization
I Lage, D Lifschitz, F Doshi-Velez, O Amir
Autonomous agents and multi-agent systems 2019, 2081, 2019
112019
Efficiently identifying individuals at high risk for treatment resistance in major depressive disorder using electronic health records
I Lage, TH McCoy Jr, RH Perlis, F Doshi-Velez
Journal of affective disorders 306, 254-259, 2022
92022
Human-in-the-loop learning of interpretable and intuitive representations
I Lage, F Doshi-Velez
Proceedings of the ICML Workshop on Human Interpretability in Machine …, 2020
92020
When does uncertainty matter
S McGrath, P Mehta, A Zytek, I Lage, H Lakkaraju
Understanding the impact of predictive uncertainty in ML assisted decision …, 2020
72020
L. wei H
O Gottesman, F Johansson, J Meier, J Dent, D Lee, S Srinivasan, L Zhang, ...
Lehman, M. Komorowski, M. Komorowski, A. Faisal, LA Celi, D. Sontag, and F …, 2018
72018
An evaluation of the human-interpretability of explanation. 2019
I Lage, E Chen, J He, M Narayanan, B Kim, S Gershman, F Doshi-Velez
CoRR abs/1902.00006.[Europe PMC free article], 1902
71902
Li wei H. Lehman, Matthieu Komorowski, Matthieu Komorowski, Aldo Faisal, Leo Anthony Celi, David Sontag, and Finale Doshi-Velez. Evaluating reinforcement learning algorithms in …
O Gottesman, F Johansson, J Meier, J Dent, D Lee, S Srinivasan, L Zhang, ...
arXiv preprint arXiv:1805.12298, 2018
52018
Do clinicians follow heuristics in prescribing antidepressants?
I Lage, MF Pradier, TH McCoy Jr, RH Perlis, F Doshi-Velez
Journal of Affective Disorders 311, 110-114, 2022
42022
Learning Human Proxy Functions to Optimize Machine Learning Systems for Sociotechnical Context
IL Lage
Harvard University, 2023
2023
(When) Are Contrastive Explanations of Reinforcement Learning Helpful?
S Narayanan, I Lage, F Doshi-Velez
arXiv preprint arXiv:2211.07719, 2022
2022
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