论文标题
贝叶斯核心：重新访问非convex优化视角
Bayesian Coresets: Revisiting the Nonconvex Optimization Perspective
论文作者
论文摘要
贝叶斯核心已经成为实施可扩展贝叶斯推断的一种有前途的方法。贝叶斯核心问题涉及选择数据样本的（加权）子集，以便使用所选子集使用的后验推断可使用完整的数据集接近后推理。该手稿通过稀疏性的镜头约束优化，重新审视贝叶斯核心。利用最新的加速优化方法的进步，我们提出和分析了一种新型算法用于核心选择。我们提供明确的收敛速率保证，并在各种基准数据集上进行经验评估，以突出我们所提出的算法与最先进的速度和准确性相比。
Bayesian coresets have emerged as a promising approach for implementing scalable Bayesian inference. The Bayesian coreset problem involves selecting a (weighted) subset of the data samples, such that the posterior inference using the selected subset closely approximates the posterior inference using the full dataset. This manuscript revisits Bayesian coresets through the lens of sparsity constrained optimization. Leveraging recent advances in accelerated optimization methods, we propose and analyze a novel algorithm for coreset selection. We provide explicit convergence rate guarantees and present an empirical evaluation on a variety of benchmark datasets to highlight our proposed algorithm's superior performance compared to state-of-the-art on speed and accuracy.
