Sitan Chen

Orcid: 0000-0002-9450-3332

According to our database1, Sitan Chen authored at least 57 papers between 2014 and 2024.

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Bibliography

2024
What does guidance do? A fine-grained analysis in a simple setting.
CoRR, 2024

Unrolled denoising networks provably learn optimal Bayesian inference.
CoRR, 2024

Predicting quantum channels over general product distributions.
CoRR, 2024

Stabilizer bootstrapping: A recipe for efficient agnostic tomography and magic estimation.
CoRR, 2024

Optimal high-precision shadow estimation.
CoRR, 2024

Faster Diffusion-based Sampling with Randomized Midpoints: Sequential and Parallel.
CoRR, 2024

Learning general Gaussian mixtures with efficient score matching.
CoRR, 2024

Provably learning a multi-head attention layer.
CoRR, 2024

An Optimal Tradeoff between Entanglement and Copy Complexity for State Tomography.
Proceedings of the 56th Annual ACM Symposium on Theory of Computing, 2024

Critical windows: non-asymptotic theory for feature emergence in diffusion models.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Optimal Tradeoffs for Estimating Pauli Observables.
Proceedings of the 65th IEEE Annual Symposium on Foundations of Computer Science, 2024

A faster and simpler algorithm for learning shallow networks.
Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023
Futility and utility of a few ancillas for Pauli channel learning.
CoRR, 2023

Learning Polynomial Transformations via Generalized Tensor Decompositions.
Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 2023

Learning Mixtures of Gaussians Using the DDPM Objective.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

The probability flow ODE is provably fast.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-type Samplers.
Proceedings of the International Conference on Machine Learning, 2023

Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

When Does Adaptivity Help for Quantum State Learning?
Proceedings of the 64th IEEE Annual Symposium on Foundations of Computer Science, 2023

Learning Narrow One-Hidden-Layer ReLU Networks.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022
Learning to predict arbitrary quantum processes.
CoRR, 2022

The Complexity of NISQ.
CoRR, 2022

Tight Bounds for State Tomography with Incoherent Measurements.
CoRR, 2022

Learning Polynomial Transformations.
CoRR, 2022

Kalman filtering with adversarial corruptions.
Proceedings of the STOC '22: 54th Annual ACM SIGACT Symposium on Theory of Computing, Rome, Italy, June 20, 2022

Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning (Very) Simple Generative Models Is Hard.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Symmetric Sparse Boolean Matrix Factorization and Applications.
Proceedings of the 13th Innovations in Theoretical Computer Science Conference, 2022

Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Tight Bounds for Quantum State Certification with Incoherent Measurements.
Proceedings of the 63rd IEEE Annual Symposium on Foundations of Computer Science, 2022

Toward Instance-Optimal State Certification With Incoherent Measurements.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021
Rethinking Algorithm Design for Modern Challenges in Data Science.
PhD thesis, 2021

Quantum advantage in learning from experiments.
CoRR, 2021

A Hierarchy for Replica Quantum Advantage.
CoRR, 2021

Efficiently Learning Any One Hidden Layer ReLU Network From Queries.
CoRR, 2021

Symmetric Boolean Factor Analysis with Applications to InstaHide.
CoRR, 2021

Algorithmic foundations for the diffraction limit.
Proceedings of the STOC '21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021

Efficiently Learning One Hidden Layer ReLU Networks From Queries.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On InstaHide, Phase Retrieval, and Sparse Matrix Factorization.
Proceedings of the 9th International Conference on Learning Representations, 2021

Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination.
Proceedings of the 62nd IEEE Annual Symposium on Foundations of Computer Science, 2021

Learning Deep ReLU Networks Is Fixed-Parameter Tractable.
Proceedings of the 62nd IEEE Annual Symposium on Foundations of Computer Science, 2021

Exponential Separations Between Learning With and Without Quantum Memory.
Proceedings of the 62nd IEEE Annual Symposium on Foundations of Computer Science, 2021

2020
On InstaHide, Phase Retrieval, and Sparse Matrix Factorization.
CoRR, 2020

Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Connections to Evolvability.
CoRR, 2020

Learning mixtures of linear regressions in subexponential time via Fourier moments.
Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, 2020

Efficiently learning structured distributions from untrusted batches.
Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, 2020

Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning Structured Distributions From Untrusted Batches: Faster and Simpler.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Entanglement is Necessary for Optimal Quantum Property Testing.
Proceedings of the 61st IEEE Annual Symposium on Foundations of Computer Science, 2020

Learning Polynomials in Few Relevant Dimensions.
Proceedings of the Conference on Learning Theory, 2020

2019
Beyond the low-degree algorithm: mixtures of subcubes and their applications.
Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, 2019

Improved Bounds for Randomly Sampling Colorings via Linear Programming.
Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, 2019

2018
Linear Programming Bounds for Randomly Sampling Colorings.
CoRR, 2018

Learning Mixtures of Product Distributions via Higher Multilinear Moments.
CoRR, 2018

2016
Basis collapse for holographic algorithms over all domain sizes.
Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, 2016

2015
Pseudorandomness for Read-Once, Constant-Depth Circuits.
CoRR, 2015

2014
Cellular Automata to More Efficiently Compute the Collatz Map.
Int. J. Unconv. Comput., 2014


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