13 citations · 43 across the 18 of their papers we have counts for
18 papers
The Squealer: Sensification of model exploration and model misfit
Andrew Gelman, Andrew H. Jaffe, Eliot Carlson +1
We introduce a method for visual and auditory feedback when exploring the fit of a model to data. Starting with a best-fit curve fit to data, the user can drag the curve to a new p…
Approximate posterior recalibration
Tiffany Cai, Philip Greengard, Ben Goodrich +1
Bayesian inference is often implemented using approximations, which can yield interval estimates that are too narrow, not fully capturing the uncertainty in the posterior distribut…
A Bayesian analysis of home advantage in professional squash
Philip Greengard, Samer Takriti
We estimate the effect of playing in one's home country in professional squash using a Bayesian hierarchical model applied to men's and women's Professional Squash Association matc…
Hierarchical Bayesian Models to Mitigate Systematic Disparities in Prediction with Proxy Outcomes
Jonas Mikhaeil, Andrew Gelman, Philip Greengard
Label bias occurs when the outcome of interest is not directly observable and instead, modeling is performed with proxy labels. When the difference between the true outcome and the…
LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model Finetuning
Han Guo, Philip Greengard, Eric P. Xing +1
We propose a simple approach for memory-efficient adaptation of pretrained language models. Our approach uses an iterative algorithm to decompose each pretrained matrix into a high…
Uniform approximation of common Gaussian process kernels using equispaced Fourier grids
Alex Barnett, Philip Greengard, Manas Rachh
The high efficiency of a recently proposed method for computing with Gaussian processes relies on expanding a (translationally invariant) covariance kernel into complex exponential…
A calibrated BISG for inferring race from surname and geolocation
Philip Greengard, Andrew Gelman
Bayesian Improved Surname Geocoding (BISG) is a ubiquitous tool for predicting race and ethnicity using an individual's geolocation and surname. Here we demonstrate that statistica…
Learning to Grow Pretrained Models for Efficient Transformer Training
Peihao Wang, Rameswar Panda, Lucas Torroba Hennigen +6
Scaling transformers has led to significant breakthroughs in many domains, leading to a paradigm in which larger versions of existing models are trained and released on a periodic…
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
Han Guo, Philip Greengard, Hongyi Wang +3
The canonical formulation of federated learning treats it as a distributed optimization problem where the model parameters are optimized against a global loss function that decompo…
Equispaced Fourier representations for efficient Gaussian process regression from a billion data points
Philip Greengard, Manas Rachh, Alex Barnett
We introduce a Fourier-based fast algorithm for Gaussian process regression in low dimensions. It approximates a translationally-invariant covariance kernel by complex exponentials…
On a linearization of quadratic Wasserstein distance
Philip Greengard, Jeremy G. Hoskins, Nicholas F. Marshall +1
This paper studies the problem of computing a linear approximation of quadratic Wasserstein distance . In particular, we compute an approximation of the negative homogeneous w…
Fast methods for posterior inference of two-group normal-normal models
Philip Greengard, Jeremy Hoskins, Charles C. Margossian +2
We describe a class of algorithms for evaluating posterior moments of certain Bayesian linear regression models with a normal likelihood and a normal prior on the regression coeffi…
Efficient Fourier representations of families of Gaussian processes
Philip Greengard
We introduce a class of algorithms for constructing Fourier representations of Gaussian processes in dimension that are valid over ranges of hyperparameter values. The scaling…
Efficient reduced-rank methods for Gaussian processes with eigenfunction expansions
Philip Greengard, Michael O'Neil
In this work we introduce a reduced-rank algorithm for Gaussian process regression. Our numerical scheme converts a Gaussian process on a user-specified interval to its Karhunen-Lo…
The piranha problem: Large effects swimming in a small pond
Christopher Tosh, Philip Greengard, Ben Goodrich +3
In some scientific fields, it is common to have certain variables of interest that are of particular importance and for which there are many studies indicating a relationship with…
A Fast Linear Regression via SVD and Marginalization
Philip Greengard, Andrew Gelman, Aki Vehtari
We describe a numerical scheme for evaluating the posterior moments of Bayesian linear regression models with partial pooling of the coefficients. The principal analytical tool of…
Zernike Polynomials: Evaluation, Quadrature, and Interpolation
Philip Greengard, Kirill Serkh
Zernike polynomials are a basis of orthogonal polynomials on the unit disk that are a natural basis for representing smooth functions. They arise in a number of applications includ…
Generalized prolate spheroidal functions: algorithms and analysis
Philip Greengard
Generalized prolate spheroidal functions (GPSFs) arise naturally in the study of bandlimited functions as the eigenfunctions of a certain truncated Fourier transform. In one dimens…