Search Results for author: Jamie Smith

Found 5 papers, 2 papers with code

Ensembling over Classifiers: a Bias-Variance Perspective

no code implementations21 Jun 2022 Neha Gupta, Jamie Smith, Ben Adlam, Zelda Mariet

Empirically, standard ensembling reducesthe bias, leading us to hypothesize that ensembles of classifiers may perform well in part because of this unexpected reduction. We conclude by an empirical analysis of recent deep learning methods that ensemble over hyperparameters, revealing that these techniques indeed favor bias reduction.

Understanding the bias-variance tradeoff of Bregman divergences

no code implementations8 Feb 2022 Ben Adlam, Neha Gupta, Zelda Mariet, Jamie Smith

We show that, similarly to the label, the central prediction can be interpreted as the mean of a random variable, where the mean operates in a dual space defined by the loss function itself.

Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure

1 code implementation23 Apr 2021 Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljačić

Bayesian optimization (BO) is a popular paradigm for global optimization of expensive black-box functions, but there are many domains where the function is not completely a black-box.

Gaussian Processes

Estimating the Spectral Density of Large Implicit Matrices

no code implementations9 Feb 2018 Ryan P. Adams, Jeffrey Pennington, Matthew J. Johnson, Jamie Smith, Yaniv Ovadia, Brian Patton, James Saunderson

However, naive eigenvalue estimation is computationally expensive even when the matrix can be represented; in many of these situations the matrix is so large as to only be available implicitly via products with vectors.

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