1.3k citations · 4.9k across the 150 of their papers we have counts for
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Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference
Jasper Tan, Blake Mason, Hamid Javadi +1
A surprising phenomenon in modern machine learning is the ability of a highly overparameterized model to generalize well (small error on the test data) even when it is trained to m…
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning
Yehuda Dar, Vidya Muthukumar, Richard G. Baraniuk
The last decade of progress in machine learning (ML), especially the deep learning era, has raised a number of scientific questions that challenge the longstanding dogma of the fie…
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics
Zichao Wang, Yi Gu, Andrew Lan +1
We propose VarFA, a variational inference factor analysis framework that extends existing factor analysis models for educational data mining to efficiently output uncertainty estim…
Max-Affine Spline Insights into Deep Generative Networks
Randall Balestriero, Sebastien Paris, Richard Baraniuk
We connect a large class of Generative Deep Networks (GDNs) with spline operators in order to derive their properties, limitations, and new opportunities. By characterizing the lat…
The Implicit Regularization of Ordinary Least Squares Ensembles
Daniel LeJeune, Hamid Javadi, Richard G. Baraniuk
Ensemble methods that average over a collection of independent predictors that are each limited to a subsampling of both the examples and features of the training data command a si…
Unsupervised Learning with Stein's Unbiased Risk Estimator
Christopher A. Metzler, Ali Mousavi, Reinhard Heckel +1
Learning from unlabeled and noisy data is one of the grand challenges of machine learning. As such, it has seen a flurry of research with new ideas proposed continuously. In this w…