23 citations · 94 across the 24 of their papers we have counts for
6 papers · 1 filter
TURF: A Two-factor, Universal, Robust, Fast Distribution Learning Algorithm
Yi Hao, Ayush Jain, Alon Orlitsky +1
Approximating distributions from their samples is a canonical statistical-learning problem. One of its most powerful and successful modalities approximates every distribution to an…
Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Discrete Distributions
Yi Hao, Alon Orlitsky
The profile of a sample is the multiset of its symbol frequencies. We show that for samples of discrete distributions, profile entropy is a fundamental measure unifying the concept…
A General Method for Robust Learning from Batches
Ayush Jain, Alon Orlitsky
In many applications, data is collected in batches, some of which are corrupt or even adversarial. Recent work derived optimal robust algorithms for estimating discrete distributio…
SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm
Yi Hao, Ayush Jain, Alon Orlitsky +1
Sample- and computationally-efficient distribution estimation is a fundamental tenet in statistics and machine learning. We present SURF, an algorithm for approximating distributio…
The Broad Optimality of Profile Maximum Likelihood
Yi Hao, Alon Orlitsky
We study three fundamental statistical-learning problems: distribution estimation, property estimation, and property testing. We establish the profile maximum likelihood (PML) esti…
Data Amplification: A Unified and Competitive Approach to Property Estimation
Yi Hao, Alon Orlitsky, Ananda T. Suresh +1
Estimating properties of discrete distributions is a fundamental problem in statistical learning. We design the first unified, linear-time, competitive, property estimator that for…