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One-Bit Quantization and Sparsification for Multiclass Linear Classification via Regularized Regression
We study the use of linear regression for multiclass classification in the over-parametrized regime where some of the training data is mislabeled.
A Novel Gaussian Min-Max Theorem and its Applications
A celebrated result by Gordon allows one to compare the min-max behavior of two Gaussian processes if certain inequality conditions are met.
Regularized Linear Regression for Binary Classification
Regularized linear regression is a promising approach for binary classification problems in which the training set has noisy labels since the regularization term can help to avoid interpolating the mislabeled data points.
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