Search Results for author:
Found 4 papers, 1 papers with code
Taking a Moment for Distributional Robustness
A rich line of recent work has studied distributionally robust learning approaches that seek to learn a hypothesis that performs well, in the worst-case, on many different distributions over a population.
Multigroup Robustness
To address the shortcomings of real-world datasets, robust learning algorithms have been designed to overcome arbitrary and indiscriminate data corruption.
Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes
We show that the sample complexity of comparative learning is characterized by the mutual VC dimension $\mathsf{VC}(S, B)$ which we define to be the maximum size of a subset shattered by both $S$ and $B$.
Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability
In this setting, we show that the sample complexity of outcome indistinguishability is characterized by the fat-shattering dimension of $D$.
