| 2024 | AAAI | Fairness under Covariate Shift: Improving Fairness-Accuracy Tradeoff with Few Unlabeled Test Samples. | Shreyas Havaldar, Jatin Chauhan, Karthikeyan Shanmugam, Jay Nandy, Aravindan Raghuveer |
| 2024 | ICLR | Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks. | Yanqiao Zhu, Jeehyun Hwang, Keir Adams, Zhen Liu, Bozhao Nan, Brock Stenfors, Yuanqi Du, Jatin Chauhan, Olaf Wiest, Olexandr Isayev, Connor W. Coley, Yizhou Sun, Wei Wang |
| 2023 | CIKM | Non-Uniform Adversarial Perturbations for Discrete Tabular Datasets. | Jay Nandy, Jatin Chauhan, Rishi Saket, Aravindan Raghuveer |
| 2023 | EMNLP | Learning under Label Proportions for Text Classification. | Jatin Chauhan, Xiaoxuan Wang, Wei Wang |
| 2022 | CIKM | Domain-Agnostic Contrastive Representations for Learning from Label Proportions. | Jay Nandy, Rishi Saket, Prateek Jain, Jatin Chauhan, Balaraman Ravindran, Aravindan Raghuveer |
| 2022 | IJCNN | BERTops: Studying BERT Representations under a Topological Lens. | Jatin Chauhan, Manohar Kaul |
| 2022 | KDD | Multi-Variate Time Series Forecasting on Variable Subsets. | Jatin Chauhan, Aravindan Raghuveer, Rishi Saket, Jay Nandy, Balaraman Ravindran |
| 2020 | ICLR | Few-Shot Learning on graphs via super-Classes based on Graph spectral Measures. | Jatin Chauhan, Deepak Nathani, Manohar Kaul |
| 2020 | IJCNN | Learning Representations using Spectral-Biased Random Walks on Graphs. | Charu Sharma, Jatin Chauhan, Manohar Kaul |
| 2019 | ACL | Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs. | Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul |