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20212026
most citedStochastic Zeroth Order Gradient and Hessian Estimators: Variance Reduction and Refined Bias Bounds

6 citations · 10 across the 10 of their papers we have counts for

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cs.LG2026

Dimension-Adaptive Batched Lipschitz Narrowing Without Knowing the Zooming Dimension

Yasong Feng

The Appropriately Combined Edge-length (ACE) sequence in A-BLiN depends on the zooming dimension . This note removes that dependence. The next edge length is selected from the…

cs.LG2023★ 1 cited

From Random Search to Bandit Learning in Metric Measure Spaces

Chuying Han, Yasong Feng, Tianyu Wang

Random Search is one of the most widely-used method for Hyperparameter Optimization, and is critical to the success of deep learning models. Despite its astonishing performance, li…

cs.LG2023★ 1 cited

A Lipschitz Bandits Approach for Continuous Hyperparameter Optimization

Yasong Feng, Weijian Luo, Yimin Huang +1

One of the most critical problems in machine learning is HyperParameter Optimization (HPO), since choice of hyperparameters has a significant impact on final model performance. Alt…

cs.LG2022★ 6 cited

Stochastic Zeroth Order Gradient and Hessian Estimators: Variance Reduction and Refined Bias Bounds

Yasong Feng, Tianyu Wang

We study stochastic zeroth order gradient and Hessian estimators for real-valued functions in . We show that, via taking finite difference along random orthogonal dir…

cs.LG2021★ 2 cited

Lipschitz Bandits with Batched Feedback

Yasong Feng, Zengfeng Huang, Tianyu Wang

In this paper, we study Lipschitz bandit problems with batched feedback, where the expected reward is Lipschitz and the reward observations are communicated to the player in batche…