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Tag: rate

  • Mixture-of-Experts under Finite-Rate Gating: Communication–Generalization Trade-offs

    Mixture-of-Experts under Finite-Rate Gating: Communication–Generalization Trade-offs arXiv:2602.15091v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures decompose prediction tasks into specialized expert sub-networks selected by a gating mechanism. This letter adopts a communication-theoretic view of MoE gating, modeling the gate as a stochastic channel operating under a finite information rate. Within an information-theoretic learning framework, we specialize…

  • Rate-optimal community detection near the KS threshold via node-robust algorithms

    Rate-optimal community detection near the KS threshold via node-robust algorithms arXiv:2511.16613v1 Announce Type: new Abstract: We study community detection in the emph{symmetric $k$-stochastic block model}, where $n$ nodes are evenly partitioned into $k$ clusters with intra- and inter-cluster connection probabilities $p$ and $q$, respectively. Our main result is a polynomial-time algorithm that achieves the minimax-optimal…

  • Learning Rate Should Scale Inversely with High-Order Data Moments in High-Dimensional Online Independent Component Analysis

    Learning Rate Should Scale Inversely with High-Order Data Moments in High-Dimensional Online Independent Component Analysis arXiv:2509.15127v1 Announce Type: new Abstract: We investigate the impact of high-order moments on the learning dynamics of an online Independent Component Analysis (ICA) algorithm under a high-dimensional data model composed of a weighted sum of two non-Gaussian random variables. This…

  • On the Rate of Gaussian Approximation for Linear Regression Problems

    On the Rate of Gaussian Approximation for Linear Regression Problems arXiv:2509.14039v1 Announce Type: new Abstract: In this paper, we consider the problem of Gaussian approximation for the online linear regression task. We derive the corresponding rates for the setting of a constant learning rate and study the explicit dependence of the convergence rate upon the…

  • Membership Inference Attacks with False Discovery Rate Control

    Membership Inference Attacks with False Discovery Rate Control arXiv:2508.07066v1 Announce Type: new Abstract: Recent studies have shown that deep learning models are vulnerable to membership inference attacks (MIAs), which aim to infer whether a data record was used to train a target model or not. To analyze and study these vulnerabilities, various MIA methods have…

  • Online Inference for Quantiles by Constant Learning-Rate Stochastic Gradient Descent

    Online Inference for Quantiles by Constant Learning-Rate Stochastic Gradient Descent arXiv:2503.02178v1 Announce Type: new Abstract: This paper proposes an online inference method of the stochastic gradient descent (SGD) with a constant learning rate for quantile loss functions with theoretical guarantees. Since the quantile loss function is neither smooth nor strongly convex, we view such SGD…