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View article: Efficient Spectral Control of Partially Observed Linear Dynamical Systems
Efficient Spectral Control of Partially Observed Linear Dynamical Systems Open
We propose a new method for the problem of controlling linear dynamical systems under partial observation and adversarial disturbances. Our new algorithm, Double Spectral Control (DSC), matches the best known regret guarantees while expone…
View article: A New Approach to Controlling Linear Dynamical Systems
A New Approach to Controlling Linear Dynamical Systems Open
We propose a new method for controlling linear dynamical systems under adversarial disturbances and cost functions. Our algorithm achieves a running time that scales polylogarithmically with the inverse of the stability margin, improving u…
View article: How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers
How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers Open
Background. A main theoretical puzzle is why over-parameterized Neural Networks (NNs) generalize well when trained to zero loss (i.e., so they interpolate the data). Usually, the NN is trained with Stochastic Gradient Descent (SGD) or one …
View article: Continual Learning in Linear Classification on Separable Data
Continual Learning in Linear Classification on Separable Data Open
We analyze continual learning on a sequence of separable linear classification tasks with binary labels. We show theoretically that learning with weak regularization reduces to solving a sequential max-margin problem, corresponding to a sp…