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Generalization for multiclass classification with overparameterized linear models
Via an overparameterized linear model with Gaussian features, we provide conditions for good generalization for multiclass classification of minimum-norm interpolating solutions in an asymptotic setting where both the number of underlying features and the number of classes scale with the number of training points.
Classification vs regression in overparameterized regimes: Does the loss function matter?
We compare classification and regression tasks in an overparameterized linear model with Gaussian features.
Blind interactive learning of modulation schemes: Multi-agent cooperation without co-design
We investigate whether learning is possible under different levels of information sharing between distributed agents which are not necessarily co-designed.
Harmless interpolation of noisy data in regression
A continuing mystery in understanding the empirical success of deep neural networks is their ability to achieve zero training error and generalize well, even when the training data is noisy and there are more parameters than data points.
