| 2025 | EMNLP | Identifying Noise in Human-Created Datasets using Training Dynamics from Generative Models. | Maeda F. Hanafi, Ishan Jindal, Yannis Katsis, Lucian Popa, Huaiyu Zhu |
| 2025 | EMNLP | Offloaded Reasoning: Efficient Inference for Large Language Models via Modular Reasoning and Refinement. | Ishan Jindal, Jayant Taneja, Chandana Badrinath, Vikas Kapur, Sachin Dev Sharma |
| 2023 | ACL | When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications. | Kevin Pei, Ishan Jindal, Kevin Chen-Chuan Chang, ChengXiang Zhai, Yunyao Li |
| 2023 | EACL | PriMeSRL-Eval: A Practical Quality Metric for Semantic Role Labeling Systems Evaluation. | Ishan Jindal, Alexandre Rademaker, Khoi-Nguyen Tran, Huaiyu Zhu, Hiroshi Kanayama, Marina Danilevsky, Yunyao Li |
| 2023 | EMNLP | Abstractive Open Information Extraction. | Kevin Pei, Ishan Jindal, Kevin Chen-Chuan Chang |
| 2023 | EMNLP | Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture. | Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh, Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James A. Hendler, Dakuo Wang |
| 2022 | CVPR | OPAD: An Optimized Policy-based Active Learning Framework for Document Content Analysis. | Sumit Shekhar, Bhanu Prakash Reddy Guda, Ashutosh Chaubey, Ishan Jindal, Avneet Jain |
| 2022 | EMNLP | Meaning Representations for Natural Languages: Design, Models and Applications. | Jeffrey Flanigan, Ishan Jindal, Yunyao Li, Tim O'Gorman, Martha Palmer, Nianwen Xue |
| 2022 | LREC | Universal Proposition Bank 2.0. | Ishan Jindal, Alexandre Rademaker, Michal Ulewicz, Ha Linh, Huyen Nguyen, Khoi-Nguyen Tran, Huaiyu Zhu, Yunyao Li |
| 2022 | NAACL | Label Definitions Improve Semantic Role Labeling. | Li Zhang, Ishan Jindal, Yunyao Li |
| 2020 | EMNLP | CLAR: A Cross-Lingual Argument Regularizer for Semantic Role Labeling. | Ishan Jindal, Yunyao Li, Siddhartha Brahma, Huaiyu Zhu |
| 2019 | CVPR | A Nonlinear, Noise-aware, Quasi-clustering Approach to Learning Deep CNNs from Noisy Labels. | Ishan Jindal, Matthew S. Nokleby, Daniel Pressel |
| 2019 | ICASSP | Tensor Matched Kronecker-structured Subspace Detection for Missing Information. | Ishan Jindal, Matthew S. Nokleby |
| 2019 | NAACL | An Effective Label Noise Model for DNN Text Classification. | Ishan Jindal, Daniel Pressel, Brian Lester, Matthew S. Nokleby |
| 2017 | ACSSC | Fast and compact Kronecker-structured dictionary learning for classification and representation. | Ishan Jindal, Matthew S. Nokleby |
| 2017 | ISIT | Performance limits on the classification of Kronecker-structured models. | Ishan Jindal, Matthew S. Nokleby |
| 2016 | ICDM | Learning Deep Networks from Noisy Labels with Dropout Regularization. | Ishan Jindal, Matthew S. Nokleby, Xue-wen Chen |