| Asymmetric LSH (ALSH) for sublinear time maximum inner product search (MIPS) A Shrivastava, P Li arXiv preprint arXiv:1405.5869, 2014 | 354 | 2014 |
| Hashing algorithms for large-scale learning P Li, A Shrivastava, J Moore, AC Konig arXiv preprint arXiv:1106.0967, 2011 | 167 | 2011 |
| Scalable and sustainable deep learning via randomized hashing R Spring, A Shrivastava Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017 | 105 | 2017 |
| Densifying one permutation hashing via rotation for fast near neighbor search A Shrivastava, P Li International Conference on Machine Learning, 557-565, 2014 | 101 | 2014 |
| Improved asymmetric locality sensitive hashing (ALSH) for maximum inner product search (MIPS) A Shrivastava, P Li arXiv preprint arXiv:1410.5410, 2014 | 89 | 2014 |
| In defense of minhash over simhash A Shrivastava, P Li Artificial Intelligence and Statistics, 886-894, 2014 | 89 | 2014 |
| Asymmetric minwise hashing for indexing binary inner products and set containment A Shrivastava, P Li Proceedings of the 24th international conference on world wide web, 981-991, 2015 | 71 | 2015 |
| Improved Densification of One Permutation Hashing A Shrivastava, P Li Uncertainty In Artificial Intelligence 2014, 2014 | 48 | 2014 |
| Simple and efficient weighted minwise hashing A Shrivastava Advances in Neural Information Processing Systems, 1498-1506, 2016 | 45* | 2016 |
| Optimal densification for fast and accurate minwise hashing A Shrivastava International Conference on Machine Learning, 3154-3163, 2017 | 42 | 2017 |
| Time Adaptive Sketches (Ada-Sketches) for Summarizing Data Streams A Shrivastava, AC Konig, M Bilenko Proceedings of the 2016 International Conference on Management of Data, 1417 …, 2016 | 39 | 2016 |
| Coding for random projections P Li, M Mitzenmacher, A Shrivastava International Conference on Machine Learning, 676-684, 2014 | 39* | 2014 |
| Fast near neighbor search in high-dimensional binary data A Shrivastava, P Li Joint European Conference on Machine Learning and Knowledge Discovery in …, 2012 | 38 | 2012 |
| A new unbiased and efficient class of lsh-based samplers and estimators for partition function computation in log-linear models R Spring, A Shrivastava arXiv preprint arXiv:1703.05160, 2017 | 33 | 2017 |
| Arrays of (locality-sensitive) count estimators (ace) anomaly detection on the edge C Luo, A Shrivastava Proceedings of the 2018 World Wide Web Conference, 1439-1448, 2018 | 28* | 2018 |
| Slide: In defense of smart algorithms over hardware acceleration for large-scale deep learning systems B Chen, T Medini, J Farwell, S Gobriel, C Tai, A Shrivastava arXiv preprint arXiv:1903.03129, 2019 | 27 | 2019 |
| Learning feasibility for task and motion planning in tabletop environments AM Wells, NT Dantam, A Shrivastava, LE Kavraki IEEE robotics and automation letters 4 (2), 1255-1262, 2019 | 27 | 2019 |
| Randomized algorithms accelerated over cpu-gpu for ultra-high dimensional similarity search Y Wang, A Shrivastava, J Wang, J Ryu Proceedings of the 2018 International Conference on Management of Data, 889-903, 2018 | 27* | 2018 |
| Extreme classification in log memory using count-min sketch: A case study of amazon search with 50m products T Medini, Q Huang, Y Wang, V Mohan, A Shrivastava arXiv preprint arXiv:1910.13830, 2019 | 26 | 2019 |
| Mission: Ultra large-scale feature selection using count-sketches A Aghazadeh, R Spring, D LeJeune, G Dasarathy, A Shrivastava International Conference on Machine Learning, 80-88, 2018 | 26 | 2018 |
