Philip Greengard

According to our database, Philip Greengard authored at least 12 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

LoRA Learns Less and Forgets Less.

Dan Biderman, Jose Javier Gonzalez Ortiz, Jacob Portes, Mansheej Paul, Philip Greengard, Connor Jennings, Daniel King, Sam Havens, Vitaliy Chiley, Jonathan Frankle, Cody Blakeney, John P. Cunningham

CoRR, 2024

LQ-LoRA: Low-rank plus Quantized Matrix Decomposition for Efficient Language Model Finetuning.

Han Guo, Philip Greengard, Eric P. Xing, Yoon Kim

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Uniform approximation of common Gaussian process kernels using equispaced Fourier grids.

Alex H. Barnett, Philip Greengard, Manas Rachh

CoRR, 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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach.

Han Guo, Philip Greengard, Hongyi Wang, Andrew Gelman, Yoon Kim, Eric P. Xing

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Efficient reduced-rank methods for Gaussian processes with eigenfunction expansions.

Philip Greengard, Michael O'Neil

Stat. Comput., 2022

A fast regression via SVD and marginalization.

Philip Greengard, Andrew Gelman, Aki Vehtari

Comput. Stat., 2022

Equispaced Fourier representations for efficient Gaussian process regression from a billion data points.

Philip Greengard, Manas Rachh, Alex H. Barnett

CoRR, 2022

On a linearization of quadratic Wasserstein distance.

Philip Greengard, Jeremy G. Hoskins, Nicholas F. Marshall, Amit Singer

CoRR, 2022

2021

Efficient Fourier representations of families of Gaussian processes.

Philip Greengard

CoRR, 2021

2020

A Fast Linear Regression via SVD and Marginalization.

Philip Greengard, Andrew Gelman, Aki Vehtari

CoRR, 2020

2019

An algorithm for the evaluation of the incomplete gamma function.

Philip Greengard, Vladimir Rokhlin

Adv. Comput. Math., 2019