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ORCIDMingxuan Li 0001
Person information
- affiliation: Columbia University, Causal Artificial Intelligence Lab, New York, NY, USA
- affiliation (2019 - 2021): Brown University, Department of Computer Science, Providence, RI, USA
- affiliation (former): Beihang University, School of Computer Science Engineering, MOE Engineering Research Center of ACAT, Beijing, China
Other persons with the same name
- Mingxuan Li — disambiguation page
- Mingxuan Li 0002
![[0000-0003-3264-0013]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0003-3264-0013")
— Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China (and 1 more)
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2020 – today
- 2025
[i5]Mingxuan Li, Junzhe Zhang, Elias Bareinboim:
Causally Aligned Curriculum Learning. CoRR abs/2503.16799 (2025)- 2024
- [c3]Mingxuan Li, Junzhe Zhang, Elias Bareinboim:
Causally Aligned Curriculum Learning. ICLR 2024 - 2023
- [i4]Jingyuan Wang, Yufan Wu, Mingxuan Li, Xin Lin, Junjie Wu, Chao Li:
Interpretability is a Kind of Safety: An Interpreter-based Ensemble for Adversary Defense. CoRR abs/2304.06919 (2023) - 2021
- [c2]Mingxuan Li, Michael L. Littman:
Towards Sample Efficient Agents through Algorithmic Alignment (Student Abstract). AAAI 2021: 15827-15828 - [i3]Yiheng Xie, Mingxuan Li, Shangqun Yu, Michael L. Littman:
Learning Generalizable Behavior via Visual Rewrite Rules. CoRR abs/2112.05218 (2021) - 2020
- [c1]Jingyuan Wang, Yufan Wu, Mingxuan Li, Xin Lin, Junjie Wu, Chao Li:
Interpretability is a Kind of Safety: An Interpreter-based Ensemble for Adversary Defense. KDD 2020: 15-24 - [i2]Mingxuan Li, Jingyuan Wang, Yufan Wu:
Detecting and Recovering Adversarial Examples: An Input Sensitivity Guided Method. CoRR abs/2002.12527 (2020) - [i1]Mingxuan Li, Michael L. Littman:
Towards Sample Efficient Agents through Algorithmic Alignment. CoRR abs/2008.03229 (2020)
Coauthor Index
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