| Hypergcn: A new method for training graph convolutional networks on hypergraphs N Yadati, M Nimishakavi, P Yadav, V Nitin, A Louis, P Talukdar NeurIPS 2019, 1511-1522, 2019 | 475 | 2019 |
| Graph convolutional networks based word embeddings P Yadav, S Vashishth, M Bhandari, P Rai, C Bhattacharyya, P Talukdar ACL 2019, 2018 | 126* | 2018 |
| TIES-merging: Resolving interference when merging models P Yadav, D Tam, L Choshen, C Raffel, M Bansal NeurIPS, 2024 | 117* | 2024 |
| NHP: Neural hypergraph link prediction N Yadati, V Nitin, M Nimishakavi, P Yadav, A Louis, P Talukdar CIKM 2020, 1705-1714, 2020 | 94* | 2020 |
| Self-Chained Image-Language Model for Video Localization and Question Answering S Yu, J Cho, P Yadav, M Bansal NeurIPS 2023, 2023 | 66 | 2023 |
| Confidence-based graph convolutional networks for semi-supervised learning S Vashishth, P Yadav, M Bhandari, P Talukdar AISTATS 2019, 1792-1801, 2019 | 46 | 2019 |
| ExplaGraphs: An explanation graph generation task for structured commonsense reasoning S Saha, P Yadav, L Bauer, M Bansal EMNLP 2021, 2021 | 43 | 2021 |
| multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning MB Swarnadeep Saha, Prateek Yadav NAACL 2021, 2021 | 26* | 2021 |
| INSPIRE: A Framework for Integrating Individual User Preferences in Recourse P Yadav, P Hase, M Bansal TMLR, 2022 | 17* | 2022 |
| Exploring continual learning for code generation models P Yadav, Q Sun, H Ding, X Li, D Zhang, M Tan, X Ma, P Bhatia, ... ACL 2023, 2023 | 15 | 2023 |
| Lovasz convolutional networks P Yadav, M Nimishakavi, N Yadati, S Vashishth, A Rajkumar, P Talukdar AISTATS 2019, 1978-1987, 2019 | 15 | 2019 |
| Pruning: Message Passing for Balancing Diversity & Difficulty in Data Pruning A Maharana, P Yadav, M Bansal The Twelfth International Conference on Learning Representations, 2024 | 10* | 2024 |
| Explanation graph generation via pre-trained language models: An empirical study with contrastive learning S Saha, P Yadav, M Bansal ACL 2022, 2022 | 8 | 2022 |
| Compeft: Compression for communicating parameter efficient updates via sparsification and quantization P Yadav, L Choshen, C Raffel, M Bansal arXiv preprint arXiv:2311.13171, 2023 | 4 | 2023 |
| Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy P Li, Z Zhang, P Yadav, YL Sung, Y Cheng, M Bansal, T Chen ICLR 2024 (Spotlight), 2023 | 4 | 2023 |
| BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions TY Zhuo, MC Vu, J Chim, H Hu, W Yu, R Widyasari, INB Yusuf, H Zhan, ... arXiv preprint arXiv:2406.15877, 2024 | 2 | 2024 |
| Aurora-m: The first open source multilingual language model red-teamed according to the us executive order T Nakamura, M Mishra, S Tedeschi, Y Chai, JT Stillerman, F Friedrich, ... arXiv preprint arXiv:2404.00399, 2024 | 2 | 2024 |
| Rank Refinement: An Algorithmic framework with applications to diversity aware influence maximization P Yadav, A Rajkumar AAAI 2021, 2021 | 2 | 2021 |
| Exclusive Supermask Subnetwork Training for Continual Learning P Yadav, M Bansal ACL (Findings) 2023, 2022 | 1 | 2022 |
| Modelling operational conditions to predict life expectancy and faults of vehicle components in a fleet A Rajkumar, S Ramakrishnan, S Banerjee, R Singh, K Chilamkurthi, ... US Patent 11,232,650, 2022 | | 2022 |
Mohit BansalParker Distinguished Professor, Computer Science, UNC Chapel Hill在 cs.unc.edu 的电子邮件经过验证
