activity
20122016
most citedUnderstanding deep learning requires rethinking generalization

1.1k citations · 1.3k across the 8 of their papers we have counts for

collaborators

12 papers

cs.LG201922 cited

Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator

Karl Krauth, Stephen Tu, Benjamin Recht

We study the sample complexity of approximate policy iteration (PI) for the Linear Quadratic Regulator (LQR), building on a recent line of work using LQR as a testbed to understand…

cs.LG20194 cited

Model Similarity Mitigates Test Set Overuse

Horia Mania, John Miller, Ludwig Schmidt +2

Excessive reuse of test data has become commonplace in today's machine learning workflows. Popular benchmarks, competitions, industrial scale tuning, among other applications, all…

cs.LG201929 cited

Learning Linear Dynamical Systems with Semi-Parametric Least Squares

Max Simchowitz, Ross Boczar, Benjamin Recht

We analyze a simple prefiltered variation of the least squares estimator for the problem of estimation with biased, semi-parametric noise, an error model studied more broadly in ca…

cs.CV2019398 cited

Do ImageNet Classifiers Generalize to ImageNet?

Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt +1

We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks have been the focus of intense research for almost a decade, raising the danger of overfitting to exc…

cs.LG20161.1k cited

Understanding deep learning requires rethinking generalization

Chiyuan Zhang, Samy Bengio, Moritz Hardt +2

Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance. Conventional wisdom attribut…

cs.LG20164 cited

KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics

Evan R. Sparks, Shivaram Venkataraman, Tomer Kaftan +2

Modern advanced analytics applications make use of machine learning techniques and contain multiple steps of domain-specific and general-purpose processing with high resource requi…