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TURF: A Two-factor, Universal, Robust, Fast Distribution Learning Algorithm
We derive a near-linear-time and essentially sample-optimal estimator that establishes $c_{t, d}=2$ for all $(t, d)\ne(1, 0)$.
Robust estimation algorithms don't need to know the corruption level
However, their vast majority approach optimal accuracy only when given a tight upper bound on the fraction of corrupt data.
SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm
Sample- and computationally-efficient distribution estimation is a fundamental tenet in statistics and machine learning.
The Limits of Maxing, Ranking, and Preference Learning
We present a comprehensive understanding of three important problems in PAC preference learning: maximum selection (maxing), ranking, and estimating all pairwise preference probabilities, in the adaptive setting.
Maxing and Ranking with Few Assumptions
PAC maximum selection (maxing) and ranking of $n$ elements via random pairwise comparisons have diverse applications and have been studied under many models and assumptions.
