Sitan Chen

Sitan Chen

Massachusetts Institute of Technology

H-index: 18

North America-United States

About Sitan Chen

Sitan Chen, With an exceptional h-index of 18 and a recent h-index of 18 (since 2020), a distinguished researcher at Massachusetts Institute of Technology, specializes in the field of theoretical computer science, high-dimensional statistics, generative modeling, quantum information.

His recent articles reflect a diverse array of research interests and contributions to the field:

Learning general Gaussian mixtures with efficient score matching

Critical windows: non-asymptotic theory for feature emergence in diffusion models

The probability flow ode is provably fast

Provably learning a multi-head attention layer

Learning narrow one-hidden-layer relu networks

Restoration-degradation beyond linear diffusions: A non-asymptotic analysis for ddim-type samplers

Learning polynomial transformations via generalized tensor decompositions

Learning mixtures of gaussians using the ddpm objective

Sitan Chen Information

University

Position

___

Citations(all)

1087

Citations(since 2020)

1077

Cited By

104

hIndex(all)

18

hIndex(since 2020)

18

i10Index(all)

24

i10Index(since 2020)

23

Email

University Profile Page

Google Scholar

Sitan Chen Skills & Research Interests

theoretical computer science

high-dimensional statistics

generative modeling

quantum information

Top articles of Sitan Chen

Learning general Gaussian mixtures with efficient score matching

arXiv preprint arXiv:2404.18893

2024/4/29

Critical windows: non-asymptotic theory for feature emergence in diffusion models

arXiv preprint arXiv:2403.01633

2024/3/3

Sitan Chen
Sitan Chen

H-Index: 5

The probability flow ode is provably fast

Advances in Neural Information Processing Systems

2024/2/13

Provably learning a multi-head attention layer

arXiv preprint arXiv:2402.04084

2024/2/6

Sitan Chen
Sitan Chen

H-Index: 5

Yuanzhi Li
Yuanzhi Li

H-Index: 1

Learning narrow one-hidden-layer relu networks

2023/7/12

Restoration-degradation beyond linear diffusions: A non-asymptotic analysis for ddim-type samplers

2023/7/3

Sitan Chen
Sitan Chen

H-Index: 5

Learning polynomial transformations via generalized tensor decompositions

2023/6/2

Learning mixtures of gaussians using the ddpm objective

Advances in Neural Information Processing Systems

2024/2/13

Kulin Shah
Kulin Shah

H-Index: 2

Sitan Chen
Sitan Chen

H-Index: 5

Learning to predict arbitrary quantum processes

PRX Quantum

2023/12/6

Sitan Chen
Sitan Chen

H-Index: 5

John Preskill
John Preskill

H-Index: 48

When does adaptivity help for quantum state learning?

2023/11/6

Efficient Pauli channel estimation with logarithmic quantum memory

arXiv preprint arXiv:2309.14326

2023/9/25

Sitan Chen
Sitan Chen

H-Index: 5

A faster and simpler algorithm for learning shallow networks

arXiv preprint arXiv:2307.12496

2023/7/24

Sitan Chen
Sitan Chen

H-Index: 5

Learning (very) simple generative models is hard

Advances in Neural Information Processing Systems

2022/12/6

Hardness of noise-free learning for two-hidden-layer neural networks

Advances in Neural Information Processing Systems

2022/12/6

Sitan Chen
Sitan Chen

H-Index: 5

Raghu Meka
Raghu Meka

H-Index: 23

Tight bounds for quantum state certification with incoherent measurements

2022/10/31

The complexity of NISQ

Nature Communications

2023/9/26

Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

arXiv preprint arXiv:2209.11215

2022/9/22

Toward instance-optimal state certification with incoherent measurements

2022/6/28

Sitan Chen
Sitan Chen

H-Index: 5

Jerry Li
Jerry Li

H-Index: 3

Quantum advantage in learning from experiments

Science

2022/6/10

Kalman filtering with adversarial corruptions

2022/6/9

See List of Professors in Sitan Chen University(Massachusetts Institute of Technology)

Co-Authors

academic-engine