166 citations · 595 across the 71 of their papers we have counts for
6 papers · 1 filter
Learning Augmentation Distributions using Transformed Risk Minimization
Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Kostas Daniilidis +1
We propose a new \emph{Transformed Risk Minimization} (TRM) framework as an extension of classical risk minimization. In TRM, we optimize not only over predictive models, but also…
Solon: Communication-efficient Byzantine-resilient Distributed Training via Redundant Gradients
Lingjiao Chen, Leshang Chen, Hongyi Wang +2
There has been a growing need to provide Byzantine-resilience in distributed model training. Existing robust distributed learning algorithms focus on developing sophisticated robus…
Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models?
Dominic Richards, Edgar Dobriban, Patrick Rebeschini
Methods for learning from data depend on various types of tuning parameters, such as penalization strength or step size. Since performance can depend strongly on these parameters,…
PAC Prediction Sets Under Covariate Shift
Sangdon Park, Edgar Dobriban, Insup Lee +1
An important challenge facing modern machine learning is how to rigorously quantify the uncertainty of model predictions. Conveying uncertainty is especially important when there a…
Consistency of invariance-based randomization tests
Edgar Dobriban
Invariance-based randomization tests -- such as permutation tests, rotation tests, or sign changes -- are an important and widely used class of statistical methods. They allow draw…
Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition
Yaodong Yu, Zitong Yang, Edgar Dobriban +2
Adversarially trained models exhibit a large generalization gap: they can interpolate the training set even for large perturbation radii, but at the cost of large test error on cle…