2 citations · 3 across the 10 of their papers we have counts for
13 papers
Unifying Privacy Accounting: Information Equivalence and Information Loss
Buxin Su, Qiaoshi Yang, Yiding Su +1
Differential privacy (DP) admits several notions, but the choice among them may affect both privacy analysis and utility. In this paper, we consider four mainstream curve-based pri…
Use and Effects of LLMs in Peer Review: A Randomized Experiment and Survey at ICML 2026
Sunnie S. Y. Kim, Wesley Hanwen Deng, Jennifer Wortman Vaughan +8
LLMs are rapidly reshaping peer review, making it important to understand how reviewers use them in practice and how different LLM-use policies affect review outcomes. We investiga…
A Sieve-Accelerated Quadrature Method for Exact Privacy Accounting in the 2020 U.S. Decennial Census
Buxin Su, Weijie Su, Chendi Wang
In 2020, the U.S. Census Bureau adopted differential privacy for the Decennial Census by injecting integer-valued Gaussian noise into published census tabulations. Exactly evaluati…
Statistical Proof as a Window into Human-AI Collaboration: Practical Insights and a Community Agenda
Xiaojing Sun, Huayu Tang, Buxin Su +4
Large language models (LLMs) are increasingly woven into expert cognitive work in daily research, yet we know little about how human expertise should adapt when an AI system can ex…
Rethinking Synthetic Rationale Supervision for Disease Prediction
Buxin Su, Bingxuan Li, Cheng Qian +3
Supervised fine-tuning with synthetic rationales is widely assumed to improve clinical prediction by teaching models not only what to predict but why. We test this assumption on fi…
Recommending Best Paper Awards for ML/AI Conferences via the Isotonic Mechanism
Garrett G. Wen, Buxin Su, Natalie Collina +2
Machine learning and artificial intelligence conferences such as NeurIPS and ICML now regularly receive tens of thousands of submissions, posing significant challenges to maintaini…
How to Find Fantastic AI Papers: Self-Rankings as a Powerful Predictor of Scientific Impact Beyond Peer Review
Buxin Su, Natalie Collina, Garrett Wen +5
Peer review in academic research aims not only to ensure factual correctness but also to identify work of high scientific potential that can shape future research directions. This…
Mitigating Privacy-Utility Trade-off in Decentralized Federated Learning via -Differential Privacy
Xiang Li, Buxin Su, Chendi Wang +2
Differentially private (DP) decentralized Federated Learning (FL) allows local users to collaborate without sharing their data with a central server. However, accurately quantifyin…
On self-training of summary data with genetic applications
Buxin Su, Jiaoyang Huang, Jin Jin +1
Prediction model training is often hindered by limited access to individual-level data due to privacy concerns and logistical challenges, particularly in biomedical research. Resam…
The 2020 US Decennial Census is more private than you (might) think
Buxin Su, Weijie J. Su, Chendi Wang
The U.S. Decennial Census serves as the foundation for many high-profile policy decision-making processes, including federal funding allocation and redistricting. In 2020, the Cens…
The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review
Buxin Su, Jiayao Zhang, Natalie Collina +6
We conducted an experiment during the review process of the 2023 International Conference on Machine Learning (ICML), asking authors with multiple submissions to rank their papers…
The Exact Risks of Reference Panel-based Regularized Estimators
Buxin Su, Qiang Sun, Xiaochen Yang +1
Reference panel-based estimators have become widely used in genetic prediction of complex traits due to their ability to address data privacy concerns and reduce computational and…
Unified Enhancement of Privacy Bounds for Mixture Mechanisms via -Differential Privacy
Chendi Wang, Buxin Su, Jiayuan Ye +2
Differentially private (DP) machine learning algorithms incur many sources of randomness, such as random initialization, random batch subsampling, and shuffling. However, such rand…