15 citations · 24 across the 6 of their papers we have counts for
6 papers
Provably learning a multi-head attention layer
Sitan Chen, Yuanzhi Li
The multi-head attention layer is one of the key components of the transformer architecture that sets it apart from traditional feed-forward models. Given a sequence length , at…
The probability flow ODE is provably fast
Sitan Chen, Sinho Chewi, Holden Lee +3
We provide the first polynomial-time convergence guarantees for the probability flow ODE implementation (together with a corrector step) of score-based generative modeling. Our ana…
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
Sitan Chen, Sinho Chewi, Jerry Li +3
We provide theoretical convergence guarantees for score-based generative models (SGMs) such as denoising diffusion probabilistic models (DDPMs), which constitute the backbone of la…
Learning (Very) Simple Generative Models Is Hard
Sitan Chen, Jerry Li, Yuanzhi Li
Motivated by the recent empirical successes of deep generative models, we study the computational complexity of the following unsupervised learning problem. For an unknown neural n…
Learning Polynomial Transformations
Sitan Chen, Jerry Li, Yuanzhi Li +1
We consider the problem of learning high dimensional polynomial transformations of Gaussians. Given samples of the form , where is hidden and $p:…
Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs
Sitan Chen, Jerry Li, Yuanzhi Li +1
Arguably the most fundamental question in the theory of generative adversarial networks (GANs) is to understand to what extent GANs can actually learn the underlying distribution.…