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2020 – today
- 2024
[c3]Han Guo, Philip Greengard, Eric P. Xing, Yoon Kim:
LQ-LoRA: Low-rank plus Quantized Matrix Decomposition for Efficient Language Model Finetuning. ICLR 2024- [i10]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:
LoRA Learns Less and Forgets Less. CoRR abs/2405.09673 (2024) - 2023
- [c2]Han Guo, Philip Greengard, Hongyi Wang, Andrew Gelman, Yoon Kim, Eric P. Xing:
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. ICLR 2023 - [c1]Peihao Wang, Rameswar Panda, Lucas Torroba Hennigen, Philip Greengard, Leonid Karlinsky, Rogério Feris, David Daniel Cox, Zhangyang Wang, Yoon Kim:
Learning to Grow Pretrained Models for Efficient Transformer Training. ICLR 2023 - [i9]Han Guo, Philip Greengard, Hongyi Wang, Andrew Gelman, Yoon Kim, Eric P. Xing:
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. CoRR abs/2302.04228 (2023) - [i8]Peihao Wang, Rameswar Panda, Lucas Torroba Hennigen, Philip Greengard, Leonid Karlinsky, Rogério Feris, David Daniel Cox, Zhangyang Wang, Yoon Kim:
Learning to Grow Pretrained Models for Efficient Transformer Training. CoRR abs/2303.00980 (2023) - [i7]Alex H. Barnett, Philip Greengard, Manas Rachh:
Uniform approximation of common Gaussian process kernels using equispaced Fourier grids. CoRR abs/2305.11065 (2023) - [i6]Han Guo, Philip Greengard, Eric P. Xing, Yoon Kim:
LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model Finetuning. CoRR abs/2311.12023 (2023) - 2022
- [j3]Philip Greengard
, Andrew Gelman, Aki Vehtari:
A fast regression via SVD and marginalization. Comput. Stat. 37(2): 701-720 (2022) - [j2]Philip Greengard, Michael O'Neil:
Efficient reduced-rank methods for Gaussian processes with eigenfunction expansions. Stat. Comput. 32(5): 94 (2022) - [i5]Philip Greengard, Jeremy G. Hoskins, Nicholas F. Marshall, Amit Singer:
On a linearization of quadratic Wasserstein distance. CoRR abs/2201.13386 (2022) - [i4]Philip Greengard, Manas Rachh, Alex H. Barnett:
Equispaced Fourier representations for efficient Gaussian process regression from a billion data points. CoRR abs/2210.10210 (2022) - 2021
- [i3]Philip Greengard, Michael O'Neil:
Efficient reduced-rank methods for Gaussian processes with eigenfunction expansions. CoRR abs/2108.05924 (2021) - [i2]Philip Greengard:
Efficient Fourier representations of families of Gaussian processes. CoRR abs/2109.14081 (2021) - 2020
- [i1]Philip Greengard, Andrew Gelman, Aki Vehtari:
A Fast Linear Regression via SVD and Marginalization. CoRR abs/2011.04829 (2020)
2010 – 2019
- 2019
- [j1]Philip Greengard, Vladimir Rokhlin:
An algorithm for the evaluation of the incomplete gamma function. Adv. Comput. Math. 45(1): 23-49 (2019)
Coauthor Index
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