42 citations · 156 across the 35 of their papers we have counts for
6 papers · 1 filter
Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions
Hongrui Chen, Holden Lee, Jianfeng Lu
We give an improved theoretical analysis of score-based generative modeling. Under a score estimate with small error (averaged across timesteps), we provide efficient converg…
Fisher information lower bounds for sampling
Sinho Chewi, Patrik Gerber, Holden Lee +1
We prove two lower bounds for the complexity of non-log-concave sampling within the framework of Balasubramanian et al. (2022), who introduced the use of Fisher information (FI) bo…
Convergence of score-based generative modeling for general data distributions
Holden Lee, Jianfeng Lu, Yixin Tan
Score-based generative modeling (SGM) has grown to be a hugely successful method for learning to generate samples from complex data distributions such as that of images and audio.…
Pitfalls of Gaussians as a noise distribution in NCE
Holden Lee, Chirag Pabbaraju, Anish Sevekari +1
Noise Contrastive Estimation (NCE) is a popular approach for learning probability density functions parameterized up to a constant of proportionality. The main idea is to design a…
Convergence for score-based generative modeling with polynomial complexity
Holden Lee, Jianfeng Lu, Yixin Tan
Score-based generative modeling (SGM) is a highly successful approach for learning a probability distribution from data and generating further samples. We prove the first polynomia…
Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods
Frederic Koehler, Holden Lee, Andrej Risteski
We consider Ising models on the hypercube with a general interaction matrix , and give a polynomial time sampling algorithm when all but eigenvalues of lie in an inte…