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ORCIDMuthu Chidambaram
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2020 – today
- 2024
[c5]Muthu Chidambaram, Rong Ge:
On the Limitations of Temperature Scaling for Distributions with Overlaps. ICLR 2024- [c4]Muthu Chidambaram, Holden Lee, Colin McSwiggen, Semon Rezchikov:
How Flawed Is ECE? An Analysis via Logit Smoothing. ICML 2024 - [i8]Muthu Chidambaram, Rong Ge:
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation. CoRR abs/2402.06855 (2024) - [i7]Muthu Chidambaram, Holden Lee, Colin McSwiggen, Semon Rezchikov:
How Flawed is ECE? An Analysis via Logit Smoothing. CoRR abs/2402.10046 (2024) - [i6]Muthu Chidambaram, Rong Ge:
Reassessing How to Compare and Improve the Calibration of Machine Learning Models. CoRR abs/2406.04068 (2024) - [i5]Muthu Chidambaram, Khashayar Gatmiry, Sitan Chen, Holden Lee, Jianfeng Lu:
What does guidance do? A fine-grained analysis in a simple setting. CoRR abs/2409.13074 (2024) - 2023
- [c3]Muthu Chidambaram, Xiang Wang, Chenwei Wu, Rong Ge:
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup. ICML 2023: 5563-5599 - [c2]Muthu Chidambaram, Chenwei Wu, Yu Cheng, Rong Ge:
Hiding Data Helps: On the Benefits of Masking for Sparse Coding. ICML 2023: 5600-5615 - [i4]Muthu Chidambaram, Chenwei Wu, Yu Cheng, Rong Ge:
Hiding Data Helps: On the Benefits of Masking for Sparse Coding. CoRR abs/2302.12715 (2023) - [i3]Muthu Chidambaram, Rong Ge:
A Uniform Confidence Phenomenon in Deep Learning and its Implications for Calibration. CoRR abs/2306.00740 (2023) - 2022
- [c1]Muthu Chidambaram, Xiang Wang, Yuzheng Hu, Chenwei Wu, Rong Ge:
Towards Understanding the Data Dependency of Mixup-style Training. ICLR 2022 - [i2]Muthu Chidambaram, Xiang Wang, Chenwei Wu, Rong Ge:
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup. CoRR abs/2210.13512 (2022) - 2021
- [i1]Muthu Chidambaram, Xiang Wang, Yuzheng Hu, Chenwei Wu, Rong Ge:
Towards Understanding the Data Dependency of Mixup-style Training. CoRR abs/2110.07647 (2021)
Coauthor Index
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