Assistant Professor, University of Massachusetts Amherst
2 papers at NeurIPS 2025
We study the tradeoff between sample complexity and round complexity in on-demand sampling.
We introduce a model of prediction with limited selectivity, and prove instance-dependent bounds on the optimal error rate.