Search Results for author:
Found 5 papers, 2 papers with code
Ensembling over Classifiers: a Bias-Variance Perspective
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
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
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.
Estimating the Spectral Density of Large Implicit Matrices
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.
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks
Our focus is on simplifying cutting edge machine learning for practitioners in order to bring such technologies into production.
