Yan Shuo Tan

According to our database¹, Yan Shuo Tan authored at least 28 papers between 2016 and 2026.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

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Links

On csauthors.net:

Bibliography

2026

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

[BibT_eX]

[DOI]

CoRR, May, 2026

Agentic-imodels: Evolving agentic interpretability tools via autoresearch.

[BibT_eX]

[DOI]

CoRR, May, 2026

On the Statistical Optimality of Optimal Decision Trees.

[BibT_eX]

[DOI]

Zineng Xu

Subhro Ghosh

Yan Shuo Tan

CoRR, March, 2026

BFTS: Thompson Sampling with Bayesian Additive Regression Trees.

[BibT_eX]

[DOI]

CoRR, February, 2026

TabClustPFN: A Prior-Fitted Network for Tabular Data Clustering.

[BibT_eX]

[DOI]

CoRR, January, 2026

Human-AI Co-design for Clinical Prediction Models.

[BibT_eX]

[DOI]

CoRR, January, 2026

2025

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

[BibT_eX]

[DOI]

CoRR, June, 2025

Revisiting Randomization in Greedy Model Search.

[BibT_eX]

[DOI]

CoRR, June, 2025

TabPFN: One Model to Rule Them All?

[BibT_eX]

[DOI]

CoRR, May, 2025

Bayesian Concept Bottleneck Models with LLM Priors.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

2024

Statistical-Computational Trade-offs for Recursive Adaptive Partitioning Estimators.

[BibT_eX]

[DOI]

Yan Shuo Tan

Jason M. Klusowski

Krishnakumar Balasubramanian

CoRR, 2024

Bayesian Concept Bottleneck Models with LLM Priors.

[BibT_eX]

[DOI]

CoRR, 2024

The Computational Curse of Big Data for Bayesian Additive Regression Trees: A Hitting Time Analysis.

[BibT_eX]

[DOI]

CoRR, 2024

2023

Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval.

[BibT_eX]

[DOI]

Yan Shuo Tan

Roman Vershynin

J. Mach. Learn. Res., 2023

Error Reduction from Stacked Regressions.

[BibT_eX]

[DOI]

Xin Chen

Jason M. Klusowski

Yan Shuo Tan

CoRR, 2023

MDI+: A Flexible Random Forest-Based Feature Importance Framework.

[BibT_eX]

[DOI]

CoRR, 2023

2022

A Mixing Time Lower Bound for a Simplified Version of BART.

[BibT_eX]

[DOI]

CoRR, 2022

Hierarchical Shrinkage: improving the accuracy and interpretability of tree-based methods.

[BibT_eX]

[DOI]

CoRR, 2022

Fast Interpretable Greedy-Tree Sums (FIGS).

[BibT_eX]

[DOI]

CoRR, 2022

Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds.

[BibT_eX]

[DOI]

Yan Shuo Tan

Abhineet Agarwal

Bin Yu

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

imodels: a python package for fitting interpretable models.

[BibT_eX]

[DOI]

J. Open Source Softw., 2021

2020

Stable discovery of interpretable subgroups via calibration in causal studies.

[BibT_eX]

[DOI]

CoRR, 2020

Curating a COVID-19 data repository and forecasting county-level death counts in the United States.

[BibT_eX]

[DOI]

CoRR, 2020

2018

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

[BibT_eX]

[DOI]

Yan Shuo Tan

Roman Vershynin

Proceedings of the Conference On Learning Theory, 2018

2017

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

[BibT_eX]

[DOI]

Yan Shuo Tan

CoRR, 2017

Phase Retrieval via Randomized Kaczmarz: Theoretical Guarantees.

[BibT_eX]

[DOI]

Yan Shuo Tan

Roman Vershynin

CoRR, 2017

2016

Energy optimization for distributions on the sphere and improvement to the Welch bounds.

[BibT_eX]

[DOI]

Yan Shuo Tan

CoRR, 2016

Yan Shuo Tan

Timeline

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Bibliography

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