1 citations · 1 across the 13 of their papers we have counts for
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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…
ACEvo: Adversarial Co-Evolution of Problem Distributions and Solvers for Combinatorial Optimization
Ruibo Duan, Yuxin Liu, Haoran Ye +3
Large language models (LLMs) are increasingly used to synthesize heuristic programs, yet most existing pipelines optimize solvers against fixed benchmark distributions. This static…
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…
A Generalized Binary Tree Mechanism for Differentially Private Approximation of All-Pair Distances
Michael Dinitz, Chenglin Fan, Jingcheng Liu +2
We study the problem of approximating all-pair distances in a weighted undirected graph with differential privacy, introduced by Sealfon [Sea16]. Given a publicly known undirected…
Inverse Materials Design by Large Language Model-Assisted Generative Framework
Yun Hao, Che Fan, Beilin Ye +7
Deep generative models hold great promise for inverse materials design, yet their efficiency and accuracy remain constrained by data scarcity and model architecture. Here, we intro…