| Random Fourier features for kernel ridge regression: Approximation bounds and statistical guarantees H Avron, M Kapralov, C Musco, C Musco, A Velingker, A Zandieh International conference on machine learning, 253-262, 2017 | 166 | 2017 |
| Beyond -approximation for submodular maximization on massive data streams A Norouzi-Fard, J Tarnawski, S Mitrović, A Zandieh, A Mousavifar, ... International Conference on Machine Learning, 3829-3838, 2018 | 95 | 2018 |
| Oblivious sketching of high-degree polynomial kernels TD Ahle, M Kapralov, JBT Knudsen, R Pagh, A Velingker, DP Woodruff, ... Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete …, 2020 | 91 | 2020 |
| A universal sampling method for reconstructing signals with simple fourier transforms H Avron, M Kapralov, C Musco, C Musco, A Velingker, A Zandieh Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing …, 2019 | 35 | 2019 |
| Kdeformer: Accelerating transformers via kernel density estimation A Zandieh, I Han, M Daliri, A Karbasi arXiv preprint arXiv:2302.02451, 2023 | 29 | 2023 |
| Dimension-independent sparse Fourier transform M Kapralov, A Velingker, A Zandieh Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete …, 2019 | 26 | 2019 |
| Scaling neural tangent kernels via sketching and random features A Zandieh, I Han, H Avron, N Shoham, C Kim, J Shin Advances in Neural Information Processing Systems 34, 1062-1073, 2021 | 24 | 2021 |
| Near input sparsity time kernel embeddings via adaptive sampling D Woodruff, A Zandieh International Conference on Machine Learning, 10324-10333, 2020 | 20 | 2020 |
| An adaptive sublinear-time block sparse Fourier transform V Cevher, M Kapralov, J Scarlett, A Zandieh Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing …, 2017 | 16 | 2017 |
| Scaling up kernel ridge regression via locality sensitive hashing A Zandieh, N Nouri, A Velingker, M Kapralov, I Razenshteyn International Conference on Artificial Intelligence and Statistics, 4088-4097, 2020 | 15* | 2020 |
| Efficiently learning Fourier sparse set functions A Amrollahi, A Zandieh, M Kapralov, A Krause Advances in Neural Information Processing Systems 32, 2019 | 15 | 2019 |
| Fast neural kernel embeddings for general activations I Han, A Zandieh, J Lee, R Novak, L Xiao, A Karbasi Advances in neural information processing systems 35, 35657-35671, 2022 | 8 | 2022 |
| Leverage score sampling for tensor product matrices in input sparsity time D Woodruff, A Zandieh International Conference on Machine Learning, 23933-23964, 2022 | 7 | 2022 |
| Hyperattention: Long-context attention in near-linear time I Han, R Jarayam, A Karbasi, V Mirrokni, DP Woodruff, A Zandieh arXiv preprint arXiv:2310.05869, 2023 | 5 | 2023 |
| Random gegenbauer features for scalable kernel methods I Han, A Zandieh, H Avron International Conference on Machine Learning, 8330-8358, 2022 | 2 | 2022 |
| Traversing the FFT Computation Tree for Dimension-Independent Sparse Fourier Transforms K Bringmann, M Kapralov, M Makarov, V Nakos, A Yagudin, A Zandieh Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms …, 2023 | 1 | 2023 |
| Sparse Fourier Transform by traversing Cooley-Tukey FFT computation graphs K Bringmann, M Kapralov, M Makarov, V Nakos, A Yagudin, A Zandieh arXiv preprint arXiv:2107.07347, 2021 | 1 | 2021 |
| Near Optimal Reconstruction of Spherical Harmonic Expansions A Zandieh, I Han, H Avron arXiv preprint arXiv:2202.12995, 2022 | | 2022 |
| Learning with Neural Tangent Kernels in Near Input Sparsity Time A Zandieh arXiv preprint arXiv:2104.00415, 2021 | | 2021 |
| Fourier Sampling in Signal Processing and Numerical Linear Algebra A Zandieh EPFL, 2020 | | 2020 |
Michael KapralovAssociate Professor, EPFLVerified email at epfl.ch
