两篇新的arXiv论文探讨了机器学习中的高级推理技术。其中一篇论文对无似然推理方法进行了基准测试，评估了它们在处理重尾和离散数据时的性能。另一篇论文则将最大似然法与最优传输相结合，用于随机块模型中的高效推理和模型选择，提出了一种用于同时恢复参数和选择聚类数量的正则化方法。 AI
影响 这些论文引入了新颖的统计方法，有望提高机器学习模型在复杂数据场景下的准确性和效率。
排序理由 两篇新的学术论文发布在arXiv上。
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arXiv:2605.30516v1 Announce Type: cross Abstract: Simulation-based inference (SBI) has become an increasingly important framework for parameter estimation in models for which simulation is feasible, including cases where likelihood evaluation is unavailable or costly. While recen…
Simulation-based inference (SBI) has become an increasingly important framework for parameter estimation in models for which simulation is feasible, including cases where likelihood evaluation is unavailable or costly. While recent work has introduced benchmark frameworks to comp…
arXiv:2605.28488v1 Announce Type: new Abstract: We study inference in stochastic block models (SBMs) through the lens of optimal transport (OT). We first establish that maximum likelihood variational inference (MLVI) can be interpreted as a semi-relaxed Gromov-Wasserstein (srGW) …
We study inference in stochastic block models (SBMs) through the lens of optimal transport (OT). We first establish that maximum likelihood variational inference (MLVI) can be interpreted as a semi-relaxed Gromov-Wasserstein (srGW) projection with entropic regularization. While t…
We study inference in stochastic block models (SBMs) through the lens of optimal transport (OT). We first establish that maximum likelihood variational inference (MLVI) can be interpreted as a semi-relaxed Gromov-Wasserstein (srGW) projection with entropic regularization. While t…