| Supporting clustering with contrastive learning D Zhang, F Nan, X Wei, S Li, H Zhu, K McKeown, R Nallapati, A Arnold, ... arXiv preprint arXiv:2103.12953, 2021 | 191 | 2021 |
| Entity-level factual consistency of abstractive text summarization F Nan, R Nallapati, Z Wang, CN Santos, H Zhu, D Zhang, K McKeown, ... arXiv preprint arXiv:2102.09130, 2021 | 143 | 2021 |
| Topic modeling with wasserstein autoencoders F Nan, R Ding, R Nallapati, B Xiang arXiv preprint arXiv:1907.12374, 2019 | 143 | 2019 |
| Pruning random forests for prediction on a budget F Nan, J Wang, V Saligrama Advances in Neural Information Processing Systems, 2334-2342, 2016 | 100 | 2016 |
| End-to-end synthetic data generation for domain adaptation of question answering systems S Shakeri, C dos Santos, H Zhu, P Ng, F Nan, Z Wang, R Nallapati, ... Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020 | 93 | 2020 |
| Adaptive Classification for Prediction Under a Budget F Nan, V Saligrama Advances in Neural Information Processing Systems, 2017 | 89 | 2017 |
| Improving factual consistency of abstractive summarization via question answering F Nan, CN Santos, H Zhu, P Ng, K McKeown, R Nallapati, D Zhang, ... arXiv preprint arXiv:2105.04623, 2021 | 88 | 2021 |
| Feature-budgeted random forest F Nan, J Wang, V Saligrama International Conference on Machine Learning, 1983-1991, 2015 | 80 | 2015 |
| Who did they respond to? conversation structure modeling using masked hierarchical transformer H Zhu, F Nan, Z Wang, R Nallapati, B Xiang Proceedings of the AAAI conference on artificial intelligence 34 (05), 9741-9748, 2020 | 39 | 2020 |
| Machine learning combining CT findings and clinical parameters improves prediction of length of stay and ICU admission in torso trauma PV Staziaki, D Wu, JC Rayan, IDO Santo, F Nan, A Maybury, ... European Radiology 31, 5434-5441, 2021 | 21 | 2021 |
| Fast margin-based cost-sensitive classification F Nan, J Wang, K Trapeznikov, V Saligrama 2014 IEEE international conference on acoustics, speech and signal …, 2014 | 21 | 2014 |
| Towards clinical encounter summarization: Learning to compose discharge summaries from prior notes HC Shing, C Shivade, N Pourdamghani, F Nan, P Resnik, D Oard, ... arXiv preprint arXiv:2104.13498, 2021 | 19 | 2021 |
| Comments on the proof of adaptive stochastic set cover based on adaptive submodularity and its implications for the group identification problem in “group-based active query … F Nan, V Saligrama IEEE Transactions on Information Theory 63 (11), 7612-7614, 2017 | 18 | 2017 |
| Evaluating the tradeoff between abstractiveness and factuality in abstractive summarization M Dreyer, M Liu, F Nan, S Atluri, S Ravi arXiv preprint arXiv:2108.02859, 2021 | 16 | 2021 |
| Answering ambiguous questions through generative evidence fusion and round-trip prediction Y Gao, H Zhu, P Ng, CN Santos, Z Wang, F Nan, D Zhang, R Nallapati, ... arXiv preprint arXiv:2011.13137, 2020 | 16 | 2020 |
| Cost aware inference for iot devices P Zhu, DAE Acar, N Feng, P Jain, V Saligrama The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 15 | 2019 |
| Analyzing the abstractiveness-factuality tradeoff with nonlinear abstractiveness constraints M Dreyer, M Liu, F Nan, S Atluri, S Ravi CoRR, abs/2108.02859, 2021 | 13 | 2021 |
| SWING: Balancing coverage and faithfulness for dialogue summarization KH Huang, S Singh, X Ma, W Xiao, F Nan, N Dingwall, WY Wang, ... arXiv preprint arXiv:2301.10483, 2023 | 12 | 2023 |
| Margin-aware unsupervised domain adaptation for cross-lingual text labeling D Zhang, R Nallapati, H Zhu, F Nan, C dos Santos, K McKeown, B Xiang Findings of the Association for Computational Linguistics: EMNLP 2020, 3527-3536, 2020 | 11 | 2020 |
| Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes S Zarbafian, M Moghadasi, A Roshandelpoor, F Nan, K Li, P Vakli, ... Scientific Reports 8 (1), 5896, 2018 | 10 | 2018 |
Venkatesh SaligramaAI Professor and Amazon ScholarVerified email at bu.edu
