An algorithm for the evaluation of the incomplete gamma function. - Philip Greengard; Vladimir Rokhlin (Adv. Comput. Math., 2019)

A Fast Linear Regression via SVD and Marginalization. - Philip Greengard; Andrew Gelman; Aki Vehtari (CoRR, 2020)

On a linearization of quadratic Wasserstein distance. - Philip Greengard; Jeremy G. Hoskins; Nicholas F. Marshall; Amit Singer (CoRR, 2022)

A fast regression via SVD and marginalization. - Philip Greengard; Andrew Gelman; Aki Vehtari (Comput. Stat., 2022)

Efficient reduced-rank methods for Gaussian processes with eigenfunction expansions. - Philip Greengard; Michael O'Neil (Stat. Comput., 2022)

Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. - Han Guo; Philip Greengard; Hongyi Wang 001; Andrew Gelman; Yoon Kim; Eric P. Xing (ICLR, 2023)

Learning to Grow Pretrained Models for Efficient Transformer Training. - Peihao Wang; Rameswar Panda; Lucas Torroba Hennigen; Philip Greengard; Leonid Karlinsky; Rogério Feris; David Daniel Cox; Zhangyang Wang; Yoon Kim (ICLR, 2023)

Uniform approximation of common Gaussian process kernels using equispaced Fourier grids. - Alex H. Barnett; Philip Greengard; Manas Rachh (CoRR, 2023)

LQ-LoRA: Low-rank plus Quantized Matrix Decomposition for Efficient Language Model Finetuning. - Han Guo; Philip Greengard; Eric P. Xing; Yoon Kim (ICLR, 2024)

LoRA Learns Less and Forgets Less. - Dan Biderman; Jacob P. Portes; Jose Javier Gonzalez Ortiz; Mansheej Paul; Philip Greengard; Connor Jennings; Daniel King; Sam Havens; Vitaliy Chiley; Jonathan Frankle; Cody Blakeney; John Patrick Cunningham (Trans. Mach. Learn. Res., 2024)

Equispaced Fourier Representations for Efficient Gaussian Process Regression from a Billion Data Points. - Philip Greengard; Manas Rachh; Alex H. Barnett (SIAM/ASA J. Uncertain. Quantification, 2025)

Efficient Fourier Representations of Families of Gaussian Processes. - Philip Greengard (SIAM J. Sci. Comput., 2025)