orlitsky
The Broad Optimality of Profile Maximum Likelihood
Neural Information Processing Systems http://nips.cc/
Data Amplification: A Unified and Competitive Approach to Property Estimation
Yi Hao, Alon Orlitsky, Ananda Theertha Suresh, Yihong Wu
Estimating properties of discrete distributions is a fundamental problem in statistical learning.
Unified Sample-Optimal Property Estimation in Near-Linear Time
Let k be the collection of distributions over the alphabet[k]:= {1,2,...,k}, and [k] be the set of finite sequences over[k].
InstanceBasedApproximationsto ProfileMaximumLikelihood
Leveraging this result, our work provides the first provably efficient implementation of PseudoPML.
Unified Sample-Optimal Property Estimation in Near-Linear Time
Neural Information Processing Systems http://nips.cc/
Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Distributions
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 concepts of estimation, inference, and compression.
The Broad Optimality of Profile Maximum Likelihood
Neural Information Processing Systems http://nips.cc/