Publications

^* Equal contribution or alphabetical ordering | ^† Student or postdoc supervised | ^‡ Corresponding author

Journal Publications

1

Statistical-Computational Trade-offs for Recursive Adaptive Partitioning Estimators

Yan Shuo Tan, Jason M Klusowski, and Krishnakumar Balasubramanian

Annals of Statistics, 2026

arXiv
2

Byzantine-Tolerant Distributed Learning of Finite Mixture Models

Qiong Zhang^*, Yan Shuo Tan^*, and Jiahua Chen

Journal of the Royal Statistical Society, Series B, 2026

arXiv
3

Fast Interpretable Greedy-Tree Sums

Yan Shuo Tan^*, Chandan Singh^*, Keyan Nasseri^*, Abhineet Agarwal^*, James Duncan, Omer Ronen, Matthew Epland, Aaron Kornblith, and Bin Yu

Proceedings of the National Academy of Sciences, vol. 122, no. 7, pp. e2310151122, 2025

arXiv
4

Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-Smooth, Non-Convex Phase Retrieval

Yan Shuo Tan and Roman Vershynin

Journal of Machine Learning Research, vol. 24, no. 58, pp. 1--47, 2023

arXiv
5

imodels: A Python Package for Fitting Interpretable Models

Chandan Singh, Keyan Nasseri, Yan Shuo Tan, Tiffany Tang, and Bin Yu

Journal of Open Source Software, vol. 6, no. 61, pp. 3192, 2021
6

Subspace Clustering Using Ensembles of K-Subspaces

John Lipor, David Hong, Yan Shuo Tan, and Laura Balzano

Information and Inference: A Journal of the IMA, vol. 10, no. 1, pp. 73--107, 2021
7

Curating a COVID-19 Data Repository and Forecasting County-Level Death Counts in the United States

Nick Altieri^*, Rebecca L Barter^*, James Duncan^*, Raaz Dwivedi^*, Karl Kumbier^*, Xiao Li^*, Robert Netzorg^*, Briton Park^*, Chandan Singh^*, Yan Shuo Tan^*, Tiffany Tang^*, Yu Wang^*, Chao Zhang^*, and Bin Yu^*

Harvard Data Science Review, 2020
8

Stable Discovery of Interpretable Subgroups via Calibration in Causal Studies

Raaz Dwivedi^*, Yan Shuo Tan^*, Briton Park, Mian Wei, Kevin Horgan, David Madigan, and Bin Yu

International Statistical Review, vol. 88, pp. S135--S178, 2020

arXiv

Invited for a special issue
9

Phase Retrieval via Randomized Kaczmarz: Theoretical Guarantees

Yan Shuo Tan and Roman Vershynin

Information and Inference: A Journal of the IMA, vol. 8, no. 1, pp. 97--123, 2019
10

Energy Optimization for Distributions on the Sphere and Improvement to the Welch Bounds

Yan Shuo Tan

Electronic Communications in Probability, 2017

Conference Publications

11

BFTS: Thompson Sampling with Bayesian Additive Regression Trees Spotlight

Ruizhe Deng^†, Bibhas Chakraborty^*, Ran Chen^*, and Yan Shuo Tan^*‡

International Conference on Machine Learning, 2026

arXiv
12

Bayesian Concept Bottleneck Models with LLM Priors

Jean Feng, Avni Kothari, Lucas Zier, Chandan Singh, and Yan Shuo Tan

Advances in Neural Information Processing Systems, 2025

arXiv
13

Hierarchical Shrinkage: Improving the Accuracy and Interpretability of Tree-Based Models Oral

Abhineet Agarwal^*, Yan Shuo Tan^*, Omer Ronen, Chandan Singh, and Bin Yu

International Conference on Machine Learning, pp. 111--135, 2022

arXiv
14

A Cautionary Tale on Fitting Decision Trees to Data from Additive Models: Generalization Lower Bounds

Yan Shuo Tan, Abhineet Agarwal, and Bin Yu

International Conference on Artificial Intelligence and Statistics, pp. 9663--9685, 2022

arXiv
15

Polynomial Time and Sample Complexity for Non-Gaussian Component Analysis: Spectral Methods

Yan Shuo Tan and Roman Vershynin

Conference on Learning Theory, pp. 498--534, 2018

Preprints

16

Agentic-imodels: Evolving Agentic Interpretability Tools via Autoresearch

Chandan Singh, Yan Shuo Tan, Weijia Xu, Zelalem Gero, Weiwei Yang, Michel Galley, and Jianfeng Gao

arXiv preprint, 2026
17

PFN-TS: Thompson Sampling for Contextual Bandits via Prior-Data Fitted Networks

Yan Shuo Tan, Kenyon Ng, Ruizhe Deng^†, Sumetha Loganathan^†, Qiong Zhang, and Bibhas Chakraborty

arXiv preprint, 2026
18

On the Statistical Optimality of Optimal Decision Trees

Zineng Xu^†, Subhroshekhar Ghosh^*, and Yan Shuo Tan^*

arXiv preprint, 2026
19

TabClustPFN: A Prior-Fitted Network for Tabular Data Clustering

Tianqi Zhao, Guanyang Wang, Yan Shuo Tan, and Qiong Zhang

arXiv preprint, 2026
20

Human-AI Co-design for Clinical Prediction Models

Jean Feng, Avni Kothari, Patrick Vossler, Andrew Bishara, Lucas Zier, Newton Addo, Aaron Kornblith, Yan Shuo Tan, and Chandan Singh

major revision at Nature Digital Medicine
21

See-in-Pairs: Reference Image-Guided Comparative Vision-Language Models for Medical Diagnosis

Ruinan Jin, Gexin Huang, Xinwei Shen, Qiong Zhang, Yan Shuo Tan, and Xiaoxiao Li

arXiv preprint, 2025
22

TabPFN: One Model to Rule Them All?

Qiong Zhang^*, Yan Shuo Tan^*, Qinglong Tian^*, and Pengfei Li

arXiv preprint, 2025
23

Revisiting Randomization in Greedy Model Search

Xin Chen^*, Jason M Klusowski^*, Yan Shuo Tan^*, and Chang Yu^*

arXiv preprint, 2025
24

On the Computational Efficiency of Bayesian Additive Regression Trees: An Asymptotic Analysis

Yan Shuo Tan, Omer Ronen, Theo Saarinen, and Bin Yu

in revision at Annals of Statistics
25

Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability

Abhineet Agarwal^*, Ana M Kenney^*, Yan Shuo Tan^*, Tiffany Tang^*, and Bin Yu

in revision at Journal of Machine Learning Research
26

Error Reduction from Stacked Regressions

Xin Chen^*, Jason M Klusowski^*, and Yan Shuo Tan^*

arXiv preprint, 2023
27

A Mixing Time Lower Bound for a Simplified Version of BART

Omer Ronen^*, Theo Saarinen^*, Yan Shuo Tan^*, James Duncan, and Bin Yu

arXiv preprint, 2022
28

Sparse Phase Retrieval via Sparse PCA Despite Model Misspecification: A Simplified and Extended Analysis

Yan Shuo Tan

arXiv preprint, 2017