4 papers
Is Multi-Distribution Learning as Easy as PAC Learning: Sharp Rates with Bounded Label Noise
Rafael Hanashiro, Abhishek Shetty, Patrick Jaillet
Towards understanding the statistical complexity of learning from heterogeneous sources, we study the problem of multi-distribution learning. Given data sources, the goal is to…
Distribution-Dependent Rates for Multi-Distribution Learning
Rafael Hanashiro, Patrick Jaillet
To address the needs of modeling uncertainty in sensitive machine learning applications, the setup of distributionally robust optimization (DRO) seeks good performance uniformly ac…
On Accelerated Perceptrons and Beyond
Guanghui Wang, Rafael Hanashiro, Etash Guha +1
The classical Perceptron algorithm of Rosenblatt can be used to find a linear threshold function to correctly classify linearly separable data points, assuming the classes are…
Linear Separation via Optimism
Rafael Hanashiro, Jacob Abernethy
Binary linear classification has been explored since the very early days of the machine learning literature. Perhaps the most classical algorithm is the Perceptron, where a weight…