| 2024 | ECAI | Graph Classification with GNNs: Optimisation, Representation & Inductive Bias. | P. Krishna Kumar, Harish G. Ramaswamy |
| 2023 | ECAI | On the Learning Dynamics of Attention Networks. | Rahul Vashisht, Harish G. Ramaswamy |
| 2022 | ACML | On the Interpretability of Attention Networks. | Lakshmi Narayan Pandey, Rahul Vashisht, Harish G. Ramaswamy |
| 2022 | ALT | Inductive Bias of Gradient Descent for Weight Normalized Smooth Homogeneous Neural Nets. | Depen Morwani, Harish G. Ramaswamy |
| 2020 | WACV | Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization. | Saurabh Desai, Harish G. Ramaswamy |
| 2019 | NAACL | On Knowledge distillation from complex networks for response prediction. | Siddhartha Arora, Mitesh M. Khapra, Harish G. Ramaswamy |
| 2016 | ICDM | Optimizing the Multiclass F-Measure via Biconcave Programming. | Harikrishna Narasimhan, Weiwei Pan, Purushottam Kar, Pavlos Protopapas, Harish G. Ramaswamy |
| 2016 | ICML | Mixture Proportion Estimation via Kernel Embeddings of Distributions. | Harish G. Ramaswamy, Clayton Scott, Ambuj Tewari |
| 2015 | ICML | Consistent Multiclass Algorithms for Complex Performance Measures. | Harikrishna Narasimhan, Harish G. Ramaswamy, Aadirupa Saha, Shivani Agarwal |
| 2015 | ICML | Convex Calibrated Surrogates for Hierarchical Classification. | Harish G. Ramaswamy, Ambuj Tewari, Shivani Agarwal |
| 2014 | COLT | On the Consistency of Output Code Based Learning Algorithms for Multiclass Learning Problems. | Harish G. Ramaswamy, Balaji Srinivasan Babu, Shivani Agarwal, Robert C. Williamson |