← Back to all news
arXiv Machine Learning By Junyi Liang, Hailiang Du

Amortized Bandwidth Learning for Kernel Density Estimation under Logarithmic Score

Read the original on arXiv Machine Learning →

The paper introduces an amortized learning framework for selecting bandwidths in kernel density estimation by optimizing the logarithmic score across a distribution of tasks. It uses a truncated-and-renormalized bounded-support formulation and affine standardization to achieve stable learning and transferability across different intervals. Experiments on Gaussian samples, a multi-family benchmark, and randomized Gaussian mixtures demonstrate that the learned selector outperforms traditional methods such as Silverman’s rule, Sheather–Jones, and least‑squares cross‑validation, especially for small or heterogeneous samples.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Aug 26

Enhancing Bayesian Optimization and Active Learning Through Kernel Diversity

The paper introduces KENDO, a unified framework that combines Ensemble Gaussian Processes with disagreement‑aware acquisition strategies to address hyperparameter selection in Bayesian optimization and active learning. By replacing costly hyperparameter sampling with a kernel ensemble and adaptive Bayesian weighting, KENDO‑BO and KENDO‑AL provide self‑correcting mechanisms tailored to their respective tasks. Experiments on synthetic and real‑world benchmarks show that KENDO‑BO matches or outperforms state‑of‑the‑art methods while cutting computational cost up to fivefold, and KENDO‑AL delivers better predictive calibration with up to 27‑times speedup compared to MCMC‑based baselines.

By Heng Zhang, Haotian Xiang, Qin Lu, Konstantinos D. Polyzos, Tara Javidi
benchmarks
More like this →
arXiv Machine Learning
Jun 9

Dendrograms of Mixing Measures for Softmax-Gated Gaussian Mixture of Experts: Consistency Without Model Sweeps

arXiv:2510. 12744v2 Announce Type: replace-cross Abstract: We develop a unified statistical framework for softmax-gated Gaussian mixture of experts (SGMoE) that addresses three long-standing obstacles in parameter estimation and model selection: (i) non-identifiability of gating parameters up to common translations, (ii) intrinsic gate-expert interactions that induce coupled differential relations in the likelihood, and (iii) the tight numerator-denominator coupling in the softmax-induced conditional density.

By Do Tien Hai, Trung Nguyen Mai, TrungTin Nguyen, Nhat Ho, Binh T. Nguyen, Christopher Drovandi
More like this →