5 citations · 17 across the 7 of their papers we have counts for
5 papers · 1 filter
Reverse Diffusion Monte Carlo
Xunpeng Huang, Hanze Dong, Yifan Hao +2
We propose a Monte Carlo sampler from the reverse diffusion process. Unlike the practice of diffusion models, where the intermediary updates -- the score functions -- are learned w…
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…
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…
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…