2 papers
cs.DS2025
Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees
Gautam Chandrasekaran, Adam R. Klivans, Lin Lin Lee +1
We give the first provably efficient algorithms for learning neural networks with distribution shift. We work in the Testable Learning with Distribution Shift framework (TDS learni…
cs.LG2025
Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random
Gautam Chandrasekaran, Vasilis Kontonis, Konstantinos Stavropoulos +1
We study the problem of PAC learning -margin halfspaces with Massart noise. We propose a simple proper learning algorithm, the Perspectron, that has sample complexity $\widetild…