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Distribution-Dependent Rates for Multi-Distribution Learning
To address the needs of modeling uncertainty in sensitive machine learning applications, the setup of distributionally robust optimization (DRO) seeks good performance uniformly across a variety of tasks.
On Accelerated Perceptrons and Beyond
The classical Perceptron algorithm of Rosenblatt can be used to find a linear threshold function to correctly classify $n$ linearly separable data points, assuming the classes are separated by some margin $\gamma > 0$.
Linear Separation via Optimism
Binary linear classification has been explored since the very early days of the machine learning literature.
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