1 citations · 1 across the 13 of their papers we have counts for
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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…
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…
Noise is All You Need: Private Second-Order Convergence of Noisy SGD
Dmitrii Avdiukhin, Michael Dinitz, Chenglin Fan +1
Private optimization is a topic of major interest in machine learning, with differentially private stochastic gradient descent (DP-SGD) playing a key role in both theory and practi…