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Author

Hao Wang

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Jul 2026

Distributional Matching for Vector Quantization: A Unified Theoretical and Empirical Framework

This work introduces principled criteria for desirable VQ behavior and demonstrates that aligning feature and code vector distributions provides a unifying mechanism for mitigating training instability and codebook collapse, and instantiate this framework using a Wasserstein-based objective with an efficient closed-form under a mild Gaussian approximation.

Xia Fang, Litao Guo, Hengchao Chen et al. · 1 citation
Preprint Aug 2026

Out-Of-The-Loop Multi-Fidelity Bayesian Optimization

This work demonstrates the suboptimality of standard MF-BO algorithms in the real-world scenarios above, even under ideal assumptions, and mitigate this problem by incorporating historical high-fidelity data accompanied by task descriptors---which can be explicitly given or extracted from unstructured metadata.

G. Sutter, Hao Wang, Luis A. Ricardez-Sandoval et al. · 0 citations

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