| Learning to grow pretrained models for efficient transformer training P Wang, R Panda, LT Hennigen, P Greengard, L Karlinsky, R Feris, ... ICLR 2023, 2023 | 39 | 2023 |
| Lora learns less and forgets less D Biderman, JG Ortiz, J Portes, M Paul, P Greengard, C Jennings, D King, ... arXiv preprint arXiv:2405.09673, 2024 | 27 | 2024 |
| LQ-LoRa: Low-rank plus quantized matrix decomposition for efficient language model finetuning H Guo, P Greengard, EP Xing, Y Kim ICLR 2024, 2023 | 24 | 2023 |
| The piranha problem: Large effects swimming in a small pond C Tosh, P Greengard, B Goodrich, A Gelman, A Vehtari, D Hsu arXiv preprint arXiv:2105.13445, 2021 | 16 | 2021 |
| An algorithm for the evaluation of the incomplete gamma function P Greengard, V Rokhlin Advances in Computational Mathematics 45, 23-49, 2019 | 15 | 2019 |
| Efficient reduced-rank methods for Gaussian processes with eigenfunction expansions P Greengard, M O’Neil Statistics and Computing 32 (5), 94, 2022 | 12 | 2022 |
| Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach H Guo, P Greengard, H Wang, A Gelman, Y Kim, EP Xing ICLR 2023, 2023 | 9 | 2023 |
| Generalized prolate spheroidal functions: algorithms and analysis P Greengard arXiv preprint arXiv:1811.02733, 2018 | 9* | 2018 |
| On a linearization of quadratic Wasserstein distance P Greengard, JG Hoskins, NF Marshall, A Singer arXiv preprint arXiv:2201.13386, 2022 | 5 | 2022 |
| Zernike Polynomials: Evaluation, Quadrature, and Interpolation P Greengard, K Serkh arXiv preprint arXiv:1811.02720, 2018 | 5 | 2018 |
| Factor clustering with t-SNE P Greengard, Y Liu, S Steinerberger, A Tsyvinski Available at SSRN 3696027, 2020 | 4 | 2020 |
| An ensemblized Metropolized Langevin sampler P Greengard Master's thesis, Courant Institute, New York University, 2015 | 4 | 2015 |
| Uniform approximation of common Gaussian process kernels using equispaced Fourier grids A Barnett, P Greengard, M Rachh Applied and Computational Harmonic Analysis 71, 101640, 2024 | 3 | 2024 |
| Equispaced Fourier representations for efficient Gaussian process regression from a billion data points P Greengard, M Rachh, A Barnett arXiv preprint arXiv:2210.10210, 2022 | 3 | 2022 |
| A fast regression via SVD and marginalization P Greengard, A Gelman, A Vehtari Computational Statistics 37 (2), 701-720, 2022 | 3 | 2022 |
| Efficient Fourier representations of families of Gaussian processes P Greengard arXiv preprint arXiv:2109.14081, 2021 | 3 | 2021 |
| An improved BISG for inferring race from surname and geolocation P Greengard, A Gelman arXiv preprint arXiv:2304.09126, 2023 | 2* | 2023 |
| Fast methods for posterior inference of two-group normal-normal models P Greengard, J Hoskins, CC Margossian, J Gabry, A Gelman, A Vehtari Bayesian Analysis 18 (3), 889-907, 2023 | 1 | 2023 |
| Approximate posterior recalibration T Cai, P Greengard, B Goodrich, A Gelman | | 2024 |
| Hierarchical Bayesian Models to Mitigate Systematic Disparities in Prediction with Proxy Outcomes J Mikhaeil, A Gelman, P Greengard arXiv preprint arXiv:2403.00639, 2024 | | 2024 |
