Feng Nan
Feng Nan
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Pruning random forests for prediction on a budget
F Nan, J Wang, V Saligrama
Advances in Neural Information Processing Systems, 2334-2342, 2016
Topic modeling with wasserstein autoencoders
F Nan, R Ding, R Nallapati, B Xiang
arXiv preprint arXiv:1907.12374, 2019
Feature-budgeted random forest
F Nan, J Wang, V Saligrama
International Conference on Machine Learning, 1983-1991, 2015
Adaptive Classification for Prediction Under a Budget
F Nan, V Saligrama
Advances in Neural Information Processing Systems, 2017
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
End-to-end synthetic data generation for domain adaptation of question answering systems
S Shakeri, CN Santos, H Zhu, P Ng, F Nan, Z Wang, R Nallapati, B Xiang
arXiv preprint arXiv:2010.06028, 2020
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
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
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
Fast margin-based cost-sensitive classification
F Nan, J Wang, K Trapeznikov, V Saligrama
2014 IEEE international conference on acoustics, speech and signal …, 2014
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
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
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
Analyzing the abstractiveness-factuality tradeoff with nonlinear abstractiveness constraints
M Dreyer, M Liu, F Nan, S Atluri, S Ravi
arXiv preprint arXiv:2108.02859, 2021
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), 1-12, 2018
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
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
Dynamic model selection for prediction under a budget
F Nan, V Saligrama
arXiv preprint arXiv:1704.07505, 2017
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
Transmitter optimization for distributed Gaussian MIMO channels
HF Chong, M Motani, F Nan
2010 Information Theory and Applications Workshop (ITA), 1-6, 2010
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