57 citations · 119 across the 20 of their papers we have counts for
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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…
Double Descent and Other Interpolation Phenomena in GANs
Lorenzo Luzi, Yehuda Dar, Richard Baraniuk
We study overparameterization in generative adversarial networks (GANs) that can interpolate the training data. We show that overparameterization can improve generalization perform…
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression
Yehuda Dar, Daniel LeJeune, Richard G. Baraniuk
We study a fundamental transfer learning process from source to target linear regression tasks, including overparameterized settings where there are more learned parameters than da…