The tree ensemble layer: Differentiability meets conditional computation H Hazimeh, N Ponomareva, P Mol, Z Tan, R Mazumder International Conference on Machine Learning, 4138-4148, 2020 | 87 | 2020 |
Shift-robust gnns: Overcoming the limitations of localized graph training data Q Zhu, N Ponomareva, J Han, B Perozzi Advances in Neural Information Processing Systems 34, 27965-27977, 2021 | 83 | 2021 |
How to dp-fy ml: A practical guide to machine learning with differential privacy N Ponomareva, H Hazimeh, A Kurakin, Z Xu, C Denison, HB McMahan, ... Journal of Artificial Intelligence Research 77, 1113-1201, 2023 | 75 | 2023 |
Conditional random fields vs. hidden markov models in a biomedical named entity recognition task N Ponomareva, P Rosso, F Pla, A Molina Proc. of Int. Conf. Recent Advances in Natural Language Processing, RANLP …, 2007 | 59 | 2007 |
Do neighbours help? an exploration of graph-based algorithms for cross-domain sentiment classification N Ponomareva, M Thelwall Proceedings of the 2012 joint conference on empirical methods in natural …, 2012 | 46 | 2012 |
Semi-supervised vs. Cross-domain Graphs for Sentiment Analysis N Ponomareva, M Thelwall RANLP 2013, 2013 | 42 | 2013 |
Accelerating gradient boosting machines H Lu, SP Karimireddy, N Ponomareva, V Mirrokni International conference on artificial intelligence and statistics, 516-526, 2020 | 40 | 2020 |
Biographies or blenders: Which resource is best for cross-domain sentiment analysis? N Ponomareva, M Thelwall International Conference on Intelligent Text Processing and Computational …, 2012 | 39 | 2012 |
Biomedical named entity recognition: a poor knowledge HMM-based approach N Ponomareva, F Pla, A Molina, P Rosso Natural Language Processing and Information Systems: 12th International …, 2007 | 39 | 2007 |
Compact multi-class boosted trees N Ponomareva, T Colthurst, G Hendry, S Haykal, S Radpour 2017 IEEE International Conference on Big Data (Big Data), 47-56, 2017 | 25 | 2017 |
Privacy-preserving recommender systems with synthetic query generation using differentially private large language models AG Carranza, R Farahani, N Ponomareva, A Kurakin, M Jagielski, M Nasr arXiv preprint arXiv:2305.05973, 2023 | 23 | 2023 |
Training text-to-text transformers with privacy guarantees N Ponomareva, J Bastings, S Vassilvitskii Findings of the Association for Computational Linguistics: ACL 2022, 2182-2193, 2022 | 23 | 2022 |
Agent prioritization for autonomous navigation KS Refaat, K Ding, N Ponomareva, S Ross 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019 | 19 | 2019 |
The study of sentiment word granularity for opinion analysis (a comparison with Maite Taboada works) O Kaurova, M Alexandrov, N Ponomareva International Journal on Social Media. MMM: Monitoring, Measurement, and …, 2010 | 14 | 2010 |
Constructing empirical models for automatic dialog parameterization M Alexandrov, X Blanco, N Ponomareva, P Rosso Text, Speech and Dialogue: 10th International Conference, TSD 2007, Pilsen …, 2007 | 14 | 2007 |
Newer is not always better: Rethinking transferability metrics, their peculiarities, stability and performance S Ibrahim, N Ponomareva, R Mazumder Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 11 | 2022 |
Air: a semi-automatic system for archiving institutional repositories N Ponomareva, JM Gomez, V Pekar Natural Language Processing and Information Systems, 169-181, 2009 | 10 | 2009 |
Fast as chita: Neural network pruning with combinatorial optimization R Benbaki, W Chen, X Meng, H Hazimeh, N Ponomareva, Z Zhao, ... International Conference on Machine Learning, 2031-2049, 2023 | 9 | 2023 |
Harnessing large-language models to generate private synthetic text A Kurakin, N Ponomareva, U Syed, L MacDermed, A Terzis arXiv preprint arXiv:2306.01684, 2023 | 9 | 2023 |
Methods, systems, and media for language identification of a media content item based on comments AS Doğruöz, N Ponomareva, CU Oehler, D Kanevsky US Patent 10,430,835, 2019 | 7 | 2019 |