11 papers
Private Approximation of Graph Spectra and Cuts via Spectral Amplifiers
Chenglin Fan, Jingcheng Liu, Pan Peng +2
We study the problem of releasing a synthetic graph that approximates the sizes of all cuts of an input graph under edge-level differential privacy. If one insists on purely additi…
Finding Differentially Private Second Order Stationary Points in Stochastic Minimax Optimization
Difei Xu, Youming Tao, Meng Ding +2
We provide the first study of the problem of finding differentially private (DP) second-order stationary points (SOSP) in stochastic (non-convex) minimax optimization. Existing lit…
Diffusion Models under Alternative Noise: Simplified Analysis and Sensitivity
Juhyeok Choi, Chenglin Fan
Diffusion models, typically formulated as discretizations of stochastic differential equations (SDEs), have achieved state-of-the-art performance in generative tasks. However, thei…
Learning-Augmented Ski Rental with Discrete Distributions: A Bayesian Approach
Bosun Kang, Hyejun Park, Chenglin Fan
We revisit the classic ski rental problem through the lens of Bayesian decision-making and machine-learned predictions. While traditional algorithms minimize worst-case cost withou…
Verifiable Exponential Mechanism for Median Estimation
Hyukjun Kwon, Chenglin Fan
Differential Privacy (DP) is a rigorous privacy standard widely adopted in data analysis and machine learning. However, its guarantees rely on correctly introducing randomized nois…
Self-Critique-Guided Curiosity Refinement: Enhancing Honesty and Helpfulness in Large Language Models via In-Context Learning
Duc Hieu Ho, Chenglin Fan
Large language models (LLMs) have demonstrated robust capabilities across various natural language tasks. However, producing outputs that are consistently honest and helpful remain…