Yuansi Chen

Affiliations:
  • Duke University, Department of Statistical Science, USA
  • ETH Zurich, Switzerland (former)


According to our database1, Yuansi Chen authored at least 16 papers between 2014 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

Online presence:

On csauthors.net:

Bibliography

2023
Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms.
CoRR, 2023

When does Metropolized Hamiltonian Monte Carlo provably outperform Metropolis-adjusted Langevin algorithm?
CoRR, 2023

A Simple Proof of the Mixing of Metropolis-Adjusted Langevin Algorithm under Smoothness and Isoperimetry.
CoRR, 2023

2022
Minimax Mixing Time of the Metropolis-Adjusted Langevin Algorithm for Log-Concave Sampling.
J. Mach. Learn. Res., 2022

Hit-and-run mixing via localization schemes.
CoRR, 2022

Localization Schemes: A Framework for Proving Mixing Bounds for Markov Chains.
CoRR, 2022

Localization Schemes: A Framework for Proving Mixing Bounds for Markov Chains (extended abstract).
Proceedings of the 63rd IEEE Annual Symposium on Foundations of Computer Science, 2022

2021
Domain adaptation under structural causal models.
J. Mach. Learn. Res., 2021

2020
Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients.
J. Mach. Learn. Res., 2020

2019
Log-concave sampling: Metropolis-Hastings algorithms are fast.
J. Mach. Learn. Res., 2019

2018
Fast MCMC Sampling Algorithms on Polytopes.
J. Mach. Learn. Res., 2018

Sampling Can Be Faster Than Optimization.
CoRR, 2018

Stability and Convergence Trade-off of Iterative Optimization Algorithms.
CoRR, 2018

2017
Vaidya walk: A sampling algorithm based on the volumetric barrier.
Proceedings of the 55th Annual Allerton Conference on Communication, 2017

2016
Self-calibrating neural networks for dimensionality reduction.
Proceedings of the 50th Asilomar Conference on Signals, Systems and Computers, 2016

2014
Fast and Robust Archetypal Analysis for Representation Learning.
Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014


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