Fast and Robust Archetypal Analysis for Representation Learning. - Yuansi Chen; Julien Mairal; Zaïd Harchaoui (CVPR, 2014)

Self-calibrating neural networks for dimensionality reduction. - Yuansi Chen; Cengiz Pehlevan; Dmitri B. Chklovskii (ACSSC, 2016)

Vaidya walk: A sampling algorithm based on the volumetric barrier. - Yuansi Chen; Raaz Dwivedi; Martin J. Wainwright; Bin Yu 001 (Allerton, 2017)

Stability and Convergence Trade-off of Iterative Optimization Algorithms. - Yuansi Chen; Chi Jin 001; Bin Yu 001 (CoRR, 2018)

Sampling Can Be Faster Than Optimization. - Yi-An Ma; Yuansi Chen; Chi Jin 001; Nicolas Flammarion; Michael I. Jordan (CoRR, 2018)

Fast MCMC Sampling Algorithms on Polytopes. - Yuansi Chen; Raaz Dwivedi; Martin J. Wainwright; Bin Yu 001 (J. Mach. Learn. Res., 2018)

Log-concave sampling: Metropolis-Hastings algorithms are fast. - Raaz Dwivedi; Yuansi Chen; Martin J. Wainwright; Bin Yu 001 (J. Mach. Learn. Res., 2019)

Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients. - Yuansi Chen; Raaz Dwivedi; Martin J. Wainwright; Bin Yu 001 (J. Mach. Learn. Res., 2020)

Domain adaptation under structural causal models. - Yuansi Chen; Peter Bühlmann (J. Mach. Learn. Res., 2021)

Localization Schemes: A Framework for Proving Mixing Bounds for Markov Chains (extended abstract). - Yuansi Chen; Ronen Eldan (FOCS, 2022)

Localization Schemes: A Framework for Proving Mixing Bounds for Markov Chains. - Yuansi Chen; Ronen Eldan (CoRR, 2022)

Hit-and-run mixing via localization schemes. - Yuansi Chen; Ronen Eldan (CoRR, 2022)

Minimax Mixing Time of the Metropolis-Adjusted Langevin Algorithm for Log-Concave Sampling. - Keru Wu; Scott C. Schmidler; Yuansi Chen (J. Mach. Learn. Res., 2022)

A Simple Proof of the Mixing of Metropolis-Adjusted Langevin Algorithm under Smoothness and Isoperimetry. - Yuansi Chen; Khashayar Gatmiry (CoRR, 2023)

When does Metropolized Hamiltonian Monte Carlo provably outperform Metropolis-adjusted Langevin algorithm? - Yuansi Chen; Khashayar Gatmiry (CoRR, 2023)

Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms. - Keru Wu; Yuansi Chen; Wooseok Ha; Bin Yu 001 (CoRR, 2023)

Regularized Dikin Walks for Sampling Truncated Logconcave Measures, Mixed Isoperimetry and Beyond Worst-Case Analysis. - Minhui Jiang; Yuansi Chen (COLT, 2025)

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts. - Wooseok Ha; Yuansi Chen (CoRR, 2025)

PolytopeWalk: Sparse MCMC Sampling over Polytopes. - Benny Sun; Yuansi Chen (J. Open Source Softw., 2025)