5 citations · 17 across the 7 of their papers we have counts for
8 papers
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…
Unsupervised Embedding of Hierarchical Structure in Euclidean Space
Jinyu Zhao, Yi Hao, Cyrus Rashtchian
Deep embedding methods have influenced many areas of unsupervised learning. However, the best methods for learning hierarchical structure use non-Euclidean representations, whereas…
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…
Unified Sample-Optimal Property Estimation in Near-Linear Time
Yi Hao, Alon Orlitsky
We consider the fundamental learning problem of estimating properties of distributions over large domains. Using a novel piecewise-polynomial approximation technique, we derive the…