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cs.LG2024
Distribution Learning with Valid Outputs Beyond the Worst-Case
Nick Rittler, Kamalika Chaudhuri
Generative models at times produce "invalid" outputs, such as images with generation artifacts and unnatural sounds. Validity-constrained distribution learning attempts to address…
cs.LG2024
Beyond Discrepancy: A Closer Look at the Theory of Distribution Shift
Robi Bhattacharjee, Nick Rittler, Kamalika Chaudhuri
Many machine learning models appear to deploy effortlessly under distribution shift, and perform well on a target distribution that is considerably different from the training dist…