| Flashattention: Fast and memory-efficient exact attention with io-awareness T Dao, D Fu, S Ermon, A Rudra, C Ré Advances in neural information processing systems 35, 16344-16359, 2022 | 3821 | 2022 |
| Combining recurrent, convolutional, and continuous-time models with linear state space layers A Gu, I Johnson, K Goel, K Saab, T Dao, A Rudra, C Ré Advances in neural information processing systems 34, 572-585, 2021 | 1144 | 2021 |
| Hippo: Recurrent memory with optimal polynomial projections A Gu, T Dao, S Ermon, A Rudra, C Ré Advances in neural information processing systems 33, 1474-1487, 2020 | 1006 | 2020 |
| Hungry hungry hippos: Towards language modeling with state space models DY Fu, T Dao, KK Saab, AW Thomas, A Rudra, C Ré arXiv preprint arXiv:2212.14052, 2022 | 794 | 2022 |
| Worst-case optimal join algorithms HQ Ngo, E Porat, C Ré, A Rudra Journal of the ACM (JACM) 65 (3), 1-40, 2018 | 507 | 2018 |
| Efficient Rijndael encryption implementation with composite field arithmetic A Rudra, PK Dubey, CS Jutla, V Kumar, JR Rao, P Rohatgi International Workshop on Cryptographic Hardware and Embedded Systems, 171-184, 2001 | 433* | 2001 |
| Skew strikes back: new developments in the theory of join algorithms HQ Ngo, C Ré, A Rudra Acm Sigmod Record 42 (4), 5-16, 2014 | 372 | 2014 |
| FAQ: questions asked frequently M Abo Khamis, HQ Ngo, A Rudra Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of …, 2016 | 274 | 2016 |
| Explicit codes achieving list decoding capacity: Error-correction with optimal redundancy V Guruswami, A Rudra IEEE Transactions on Information Theory 54 (1), 135-150, 2008 | 272 | 2008 |
| Online learning in online auctions A Blum, V Kumar, A Rudra, F Wu Theoretical Computer Science 324 (2-3), 137-146, 2004 | 251 | 2004 |
| Essential coding theory V Guruswami, A Rudra, M Sudan Draft available at http://www. cse. buffalo. edu/atri/courses/coding-theory …, 2012 | 220 | 2012 |
| Scatterbrain: Unifying sparse and low-rank attention B Chen, T Dao, E Winsor, Z Song, A Rudra, C Ré Advances in Neural Information Processing Systems 34, 17413-17426, 2021 | 197 | 2021 |
| How to train your hippo: State space models with generalized orthogonal basis projections A Gu, I Johnson, A Timalsina, A Rudra, C Ré arXiv preprint arXiv:2206.12037, 2022 | 194 | 2022 |
| Efficiently decodable non-adaptive group testing P Indyk, HQ Ngo, A Rudra Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete …, 2010 | 188 | 2010 |
| Approximating matches made in heaven N Chen, N Immorlica, AR Karlin, M Mahdian, A Rudra International colloquium on automata, languages, and programming, 266-278, 2009 | 159 | 2009 |
| Ordering by weighted number of wins gives a good ranking for weighted tournaments D Coppersmith, L Fleischer, A Rudra Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete …, 2006 | 156 | 2006 |
| Learning fast algorithms for linear transforms using butterfly factorizations T Dao, A Gu, M Eichhorn, A Rudra, C Ré International conference on machine learning, 1517-1527, 2019 | 155 | 2019 |
| When LP Is the Cure for Your Matching Woes: Improved Bounds for Stochastic Matchings N Bansal, A Gupta, J Li, J Mestre, V Nagarajan, A Rudra Algorithmica, 1-30, 2012 | 147 | 2012 |
| Zoology: Measuring and improving recall in efficient language models S Arora, S Eyuboglu, A Timalsina, I Johnson, M Poli, J Zou, A Rudra, C Ré arXiv preprint arXiv:2312.04927, 2023 | 146 | 2023 |
| Monarch: Expressive structured matrices for efficient and accurate training T Dao, B Chen, NS Sohoni, A Desai, M Poli, J Grogan, A Liu, A Rao, ... International Conference on Machine Learning, 4690-4721, 2022 | 143 | 2022 |
Christopher RéComputer Science, Stanford UniversityVerified email at cs.stanford.edu
