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ORCIDMaggie Makar
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2020 – today
- 2024
[c12]Chinmaya Kausik, Yangyi Lu, Kevin Tan
, Maggie Makar, Yixin Wang, Ambuj Tewari:
Offline Policy Evaluation and Optimization Under Confounding. AISTATS 2024: 1459-1467- [c11]Fahad Kamran, Maggie Makar, Jenna Wiens:
Learning to Rank for Optimal Treatment Allocation Under Resource Constraints. AISTATS 2024: 3727-3735 - [i9]Claudia Shi, Nicolas Beltran-Velez, Achille Nazaret, Carolina Zheng, Adrià Garriga-Alonso, Andrew Jesson, Maggie Makar, David M. Blei:
Hypothesis Testing the Circuit Hypothesis in LLMs. CoRR abs/2410.13032 (2024) - 2023
- [j1]Maggie Makar, Alexander D'Amour:
Fairness and robustness in anti-causal prediction. Trans. Mach. Learn. Res. 2023 (2023) - [c10]Jiaai Xu, Rada Mihalcea, Elena Frank, Srijan Sen, Maggie Makar:
Uncovering the Varied Impact of Behavioral Change Messages on Population Groups. MLHC 2023: 906-922 - [i8]Shengpu Tang, Maggie Makar, Michael W. Sjoding, Finale Doshi-Velez, Jenna Wiens:
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare. CoRR abs/2305.01738 (2023) - [i7]Emily Mu, John V. Guttag, Maggie Makar:
Multi-Similarity Contrastive Learning. CoRR abs/2307.02712 (2023) - [i6]Ron Nafshi, Maggie Makar:
Partial identification of kernel based two sample tests with mismeasured data. CoRR abs/2308.03570 (2023) - 2022
- [c9]Maggie Makar, Ben Packer, Dan Moldovan, Davis W. Blalock, Yoni Halpern, Alexander D'Amour:
Causally motivated shortcut removal using auxiliary labels. AISTATS 2022: 739-766 - [c8]Jiayun Zheng, Maggie Makar:
Causally Motivated Multiple Shortcut Removal Using High Dimensional Auxiliary Labels. AMIA 2022 - [c7]Shengpu Tang, Maggie Makar, Michael W. Sjoding, Finale Doshi-Velez, Jenna Wiens:
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare. NeurIPS 2022 - [c6]Jiaxuan Wang, Sarah Jabbour, Maggie Makar, Michael W. Sjoding, Jenna Wiens:
Learning Concept Credible Models for Mitigating Shortcuts. NeurIPS 2022 - [c5]Jiayun Zheng, Maggie Makar:
Causally motivated multi-shortcut identification and removal. NeurIPS 2022 - [i5]Maggie Makar, Alexander D'Amour:
Fairness and robustness in anti-causal prediction. CoRR abs/2209.09423 (2022) - 2021
- [b1]Maggie Makar:
Machine learning and causality: Building efficient, and reliable models for decision-making. Massachusetts Institute of Technology, USA, 2021 - [c4]Maggie Makar, Lauren West, David Hooper, Eric Horvitz, Erica Shenoy, John V. Guttag:
Exploiting structured data for learning contagious diseases under incomplete testing. ICML 2021: 7348-7357 - [i4]Maggie Makar, Ben Packer, Dan Moldovan, Davis W. Blalock, Yoni Halpern, Alexander D'Amour:
Causally-motivated Shortcut Removal Using Auxiliary Labels. CoRR abs/2105.06422 (2021) - 2020
- [c3]Maggie Makar, Fredrik D. Johansson, John V. Guttag, David A. Sontag:
Estimation of Bounds on Potential Outcomes For Decision Making. ICML 2020: 6661-6671
2010 – 2019
- 2019
- [c2]Maggie Makar, Adith Swaminathan, Emre Kiciman:
A Distillation Approach to Data Efficient Individual Treatment Effect Estimation. AAAI 2019: 4544-4551 - [i3]Maggie Makar, Fredrik D. Johansson, John V. Guttag, David A. Sontag:
Estimation of Utility-Maximizing Bounds on Potential Outcomes. CoRR abs/1910.04817 (2019) - 2018
- [c1]Maggie Makar, John V. Guttag, Jenna Wiens:
Learning the Probability of Activation in the Presence of Latent Spreaders. AAAI 2018: 134-141 - 2017
- [i2]Maggie Makar, John V. Guttag, Jenna Wiens:
Learning the Probability of Activation in the Presence of Latent Spreaders. CoRR abs/1712.00643 (2017) - [i1]Maggie Makar, Marzyeh Ghassemi, David Cutler, Ziad Obermeyer:
Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data. CoRR abs/1712.00644 (2017)
Coauthor Index
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