Towards Understanding the Data Dependency of Mixup-style Training. - Muthu Chidambaram; Xiang Wang 011; Yuzheng Hu; Chenwei Wu 002; Rong Ge 001 (ICLR, 2022)

Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup. - Muthu Chidambaram; Xiang Wang 011; Chenwei Wu 002; Rong Ge 001 (ICML, 2023)

Hiding Data Helps: On the Benefits of Masking for Sparse Coding. - Muthu Chidambaram; Chenwei Wu 002; Yu Cheng 002; Rong Ge 001 (ICML, 2023)

A Uniform Confidence Phenomenon in Deep Learning and its Implications for Calibration. - Muthu Chidambaram; Rong Ge 001 (CoRR, 2023)

On the Limitations of Temperature Scaling for Distributions with Overlaps. - Muthu Chidambaram; Rong Ge 001 (ICLR, 2024)

How Flawed Is ECE? An Analysis via Logit Smoothing. - Muthu Chidambaram; Holden Lee; Colin McSwiggen; Semon Rezchikov (ICML, 2024)

What does guidance do? A fine-grained analysis in a simple setting. - Muthu Chidambaram; Khashayar Gatmiry; Sitan Chen; Holden Lee; Jianfeng Lu 001 (NeurIPS, 2024)

Reassessing How to Compare and Improve the Calibration of Machine Learning Models. - Muthu Chidambaram; Rong Ge 001 (ICLR, 2025)

For Better or For Worse? Learning Minimum Variance Features With Label Augmentation. - Muthu Chidambaram; Rong Ge 001 (ICLR, 2025)