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Z₂ 同步中 TAP 自由能的局部凸性与 AMP 收敛AI 解读

We study mean-field variational Bayesian inference using the TAP approach, for Z2-synchronization as a prototypical example of a high-dimensional Bayesian model. We show that for any signal strength λ>1 (the weak-recovery threshold), there exists a unique local minimizer of the TAP free energy functional near the mean of the Bayes posterior law. Furthermore, the TAP free energy in a local neighborhood of this minimizer is strongly convex. Consequently, a…

关于“使用 ReLU 深度神经网络进行非参数回归”的讨论AI 解读

We congratulate Johannes Schmidt-Hieber for his elegant and thought-provoking results.His article uses deep-learning-inspired methods in the context of nonparametric regression.Schmidt-Hieber defines a rich class of composition-based functions G(q, d, t, β, K) and a class of sparse multilayer neural networks F(L, p, s, F ).He proves that least squares estimation over the class of sparse neural networks (with suitably chosen architecture (L, p, s, F )) achieves nearly minimax…

非凸损失下经验风险的几何结构AI 解读

Most high-dimensional estimation methods propose to minimize a cost function (empirical risk) that is a sum of losses associated to each data point (each example). In this paper, we focus on the case of nonconvex losses. Classical empirical process theory implies uniform convergence of the empirical (or sample) risk to the population risk. While under additional assumptions, uniform convergence implies consistency of the resulting M-estimator, it does not ensure that the…