Methods and Analysis of The First Competition in Predicting Generalization of Deep Learning. - Yiding Jiang; Parth Natekar; Manik Sharma; Sumukh K. Aithal; Dhruva Kashyap; Natarajan Subramanyam; Carlos Lassance; Daniel M. Roy 001; Gintare Karolina Dziugaite; Suriya Gunasekar; Isabelle Guyon; Pierre Foret; et al. (NeurIPS (Competition and Demos), 2020)

Transfer Learning using Neural Ordinary Differential Equations. - Rajath S; Sumukh K. Aithal; Natarajan Subramanyam (CoRR, 2020)

S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation. - Harsh Rangwani; Arihant Jain; Sumukh K. Aithal; R. Venkatesh Babu (ICCV, 2021)

Domain Shift in Capsule Networks. - Rajath S; Sumukh K. Aithal; Natarajan Subramanyam (ICPRAM, 2021)

Robustness to Augmentations as a Generalization metric. - Sumukh K. Aithal; Dhruva Kashyap; Natarajan Subramanyam (CoRR, 2021)

A Closer Look at Smoothness in Domain Adversarial Training. - Harsh Rangwani; Sumukh K. Aithal; Mayank Mishra; Arihant Jain; Venkatesh Babu Radhakrishnan (ICML, 2022)

Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data. - Harsh Rangwani; Sumukh K. Aithal; Mayank Mishra; Venkatesh Babu R. (NeurIPS, 2022)

Leveraging the Third Dimension in Contrastive Learning. - Sumukh K. Aithal; Anirudh Goyal; Alex Lamb; Yoshua Bengio; Michael Mozer (CoRR, 2023)

Understanding Hallucinations in Diffusion Models through Mode Interpolation. - Sumukh K. Aithal; Pratyush Maini; Zachary C. Lipton; J. Zico Kolter (NeurIPS, 2024)