8 papers
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…
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…
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…
Learning Augmented Graph -Clustering
Chenglin Fan, Kijun Shin
Clustering is a fundamental task in unsupervised learning. Previous research has focused on learning-augmented -means in Euclidean metrics, limiting its applicability to complex…
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…
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…