Publications

GREATS: Online Selection of High-Quality Data for LLM Training in Every Iteration

NeurIPS'24
Spotlight Presentation (top ~3% among submissions)
Paper

Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits

ICML'24
Oral Presentation (top ~1.5% among submissions)
Paper

Efficient Data Valuation for Weighted Nearest Neighbor Algorithms

AISTATS'24
Oral Presentation (top ~1.5% among submissions)
Paper

Privacy-Preserving In-Context Learning for Large Language Models

ICLR'24
Paper

DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer

ICLR'24
Spotlight Presentation (top ~3% among submissions)
Paper

Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation

NeurIPS'23
Spotlight Presentation (top ~3% among submissions)
Paper

A Randomized Approach for Tight Privacy Accounting

NeurIPS'23
Paper

Data Banzhaf: A Robust Data Valuation Framework for Machine Learning

AISTATS'23
Oral Presentation (top ~1.5% among submissions)
Paper

LAVA: Data Valuation without Pre-Specified Learning Algorithms

ICLR'23
Spotlight Presentation (top ~3% among submissions)
Paper

Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning

NeurIPS'22
Paper

Improving Cooperative Game Theory-based Data Valuation via Data Utility Learning

ICLR 2022 Workshop on Socially Responsible Machine Learning
Paper

Concurrent Composition of Differential Privacy

TCC'21, TPDP'21
Paper

DPlis: Boosting Utility of Differentially Private Deep Learning via Randomized Smoothing

PETS'21 (acceptance rate: 19%)
Paper

RIGA: Covert and Robust White-Box Watermarking of Deep Neural Networks

WWW'21 (acceptance rate: 20.6%)
Paper

Improving Robustness to Model Inversion Attacks via Mutual Information Regularization

AAAI'21 (acceptance rate: 21%)
Paper

A Principled Approach to Data Valuation for Federated Learning

Federated Learning: Privacy and Incentives (2020)
Paper