| Asymmetric LSH (ALSH) for sublinear time maximum inner product search (MIPS) A Shrivastava, P Li Advances in neural information processing systems 27, 2014 | 593 | 2014 |
| Deja vu: Contextual sparsity for efficient llms at inference time Z Liu, J Wang, T Dao, T Zhou, B Yuan, Z Song, A Shrivastava, C Zhang, ... International Conference on Machine Learning, 22137-22176, 2023 | 263 | 2023 |
| Hashing algorithms for large-scale learning P Li, A Shrivastava, J Moore, A König Advances in neural information processing systems 24, 2011 | 201 | 2011 |
| Scissorhands: Exploiting the persistence of importance hypothesis for llm kv cache compression at test time Z Liu, A Desai, F Liao, W Wang, V Xie, Z Xu, A Kyrillidis, A Shrivastava Advances in Neural Information Processing Systems 36, 52342-52364, 2023 | 156 | 2023 |
| Scalable and sustainable deep learning via randomized hashing R Spring, A Shrivastava Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017 | 155 | 2017 |
| In defense of minhash over simhash A Shrivastava, P Li Artificial intelligence and statistics, 886-894, 2014 | 138 | 2014 |
| Improved asymmetric locality sensitive hashing (ALSH) for maximum inner product search (MIPS) A Shrivastava, P Li arXiv preprint arXiv:1410.5410, 2014 | 134 | 2014 |
| Slide: In defense of smart algorithms over hardware acceleration for large-scale deep learning systems B Chen, T Medini, J Farwell, C Tai, A Shrivastava Proceedings of Machine Learning and Systems 2, 291-306, 2020 | 132 | 2020 |
| Densifying one permutation hashing via rotation for fast near neighbor search A Shrivastava, P Li International Conference on Machine Learning, 557-565, 2014 | 130 | 2014 |
| 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 | 110 | 2019 |
| 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 | 98 | 2015 |
| Extreme classification in log memory using count-min sketch: A case study of amazon search with 50m products TKR Medini, Q Huang, Y Wang, V Mohan, A Shrivastava Advances in Neural Information Processing Systems 32, 2019 | 93 | 2019 |
| Privacy adversarial network: representation learning for mobile data privacy S Liu, J Du, A Shrivastava, L Zhong Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous …, 2019 | 83* | 2019 |
| Mongoose: A learnable lsh framework for efficient neural network training B Chen, Z Liu, B Peng, Z Xu, JL Li, T Dao, Z Song, A Shrivastava, C Re International Conference on Learning Representations, 2020 | 78 | 2020 |
| Optimal densification for fast and accurate minwise hashing A Shrivastava International Conference on Machine Learning, 3154-3163, 2017 | 77 | 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 | 67 | 2016 |
| Simple and efficient weighted minwise hashing A Shrivastava Advances in Neural Information Processing Systems, 1498-1506, 2016 | 66* | 2016 |
| 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 | 64 | 2018 |
| Improved Densification of One Permutation Hashing A Shrivastava, P Li Uncertainty In Artificial Intelligence 2014, 2014 | 64 | 2014 |
| Adaptive learned bloom filter (ada-bf): Efficient utilization of the classifier with application to real-time information filtering on the web Z Dai, A Shrivastava Advances in neural information processing systems 33, 11700-11710, 2020 | 57* | 2020 |
