Search Results for author: Vaishakh Ravindrakumar

Found 5 papers, 0 papers with code

TURF: A Two-factor, Universal, Robust, Fast Distribution Learning Algorithm

no code implementations15 Feb 2022 Yi Hao, Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar

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

no code implementations11 Feb 2022 Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar

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

no code implementations NeurIPS 2020 Yi Hao, Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar

Sample- and computationally-efficient distribution estimation is a fundamental tenet in statistics and machine learning.

The Limits of Maxing, Ranking, and Preference Learning

no code implementations ICML 2018 Moein Falahatgar, Ayush Jain, Alon Orlitsky, Venkatadheeraj Pichapati, Vaishakh Ravindrakumar

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

no code implementations NeurIPS 2017 Moein Falahatgar, Yi Hao, Alon Orlitsky, Venkatadheeraj Pichapati, Vaishakh Ravindrakumar

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.

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