| Submodularity in Data Subset Selection and Active Learning K Wei, R Iyer, J Bilmes International Conference on Machine Learning, 1954–1963, 2015 | 476 | 2015 |
| Submodular subset selection for large-scale speech training data K Wei, Y Liu, K Kirchhoff, C Bartels, J Bilmes 2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014 | 130 | 2014 |
| Fast multi-stage submodular maximization K Wei, R Iyer, J Bilmes International conference on machine learning, 1494-1502, 2014 | 108 | 2014 |
| Using document summarization techniques for speech data subset selection K Wei, Y Liu, K Kirchhoff, J Bilmes Proceedings of the 2013 Conference of the North American Chapter of the …, 2013 | 96 | 2013 |
| Submodular feature selection for high-dimensional acoustic score spaces Y Liu, K Wei, K Kirchhoff, Y Song, J Bilmes 2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013 | 77 | 2013 |
| Unsupervised submodular subset selection for speech data K Wei, Y Liu, K Kirchhoff, J Bilmes 2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014 | 67 | 2014 |
| Algorithms for optimizing the ratio of submodular functions W Bai, R Iyer, K Wei, J Bilmes International Conference on Machine Learning, 2751-2759, 2016 | 50 | 2016 |
| Mixed robust/average submodular partitioning: Fast algorithms, guarantees, and applications K Wei, RK Iyer, S Wang, W Bai, JA Bilmes Advances in Neural Information Processing Systems 28, 2015 | 45 | 2015 |
| Choosing panels of genomics assays using submodular optimization K Wei, M Libbrecht, J Bilmes, W Noble Genome Biology 17, 229, 2016 | 17 | 2016 |
| How to intelligently distribute training data to multiple compute nodes: Distributed machine learning via submodular partitioning K Wei, R Iyer, S Wang, W Bai, J Bilmes Neural Information Processing Society (NIPS) Workshop, Montreal, Canada, 2015 | 8 | 2015 |
| A practical online framework for extracting running video summaries under a fixed memory budget C Lavania, K Wei, R Iyer, J Bilmes Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021 | 5 | 2021 |
| Modeling and Simultaneously Removing Bias via Adversarial Neural Networks J Moore, J Pfeiffer, K Wei, R Iyer, D Charles, R Gilad-Bachrach, L Boyles, ... arXiv preprint arXiv:1804.06909, 2018 | 5 | 2018 |
| Jensen: An easily-extensible c++ toolkit for production-level machine learning and convex optimization R Iyer, JT Halloran, K Wei arXiv preprint arXiv:1807.06574, 2018 | 4 | 2018 |
| A submodularity framework for data subset selection K Kirchhoff, J Bilmes, K Wei, Y Liu, A Mandal, C Bartels Technical Report AFRL-RH-WP-TR-2013-0108, 2013 | 4 | 2013 |
| Submodular Optimization and Data Processing K Wei | 2 | 2016 |
| Mixed robust/average submodular partitioning: Fast algorithms, guarantees, and applications to parallel machine learning and multi-label image segmentation K Wei, R Iyer, S Wang, W Bai, J Bilmes arXiv preprint arXiv:1510.08865, 2015 | 1 | 2015 |
| Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications: NIPS 2015 Extended Supplementary K Wei, R Iyer, S Wang, W Bai, J Bilmes arXiv preprint arXiv:1510.08865, 2015 | | 2015 |
| Using Document Summarization Techniques for Speech Data Subset K Wei, Y Liu, K Kirchhoff, J Bilmes | | |
| Submodularity in Data Subset Selection and Active Learning: Extended Version K Wei, W EDU, R Iyer, J Bilmes | | |
| Mixed Robust/Average Submodular Partitioning K Wei, R Iyer, S Wang, W Bai, J Bilmes | | |
