Iterative thresholding for non-linear learning in the strong $\varepsilon$-contamination model

• URL: http://arxiv.org/abs/2409.03703v1
• Date: Thu, 5 Sep 2024 16:59:56 GMT
• Title: Iterative thresholding for non-linear learning in the strong $\varepsilon$-contamination model
• Authors: Arvind Rathnashyam, Alex Gittens,
• Abstract summary: We derive approximation bounds for learning single neuron models using thresholded descent. We also study the linear regression problem, where $sigma(mathbfx) = mathbfx$.
• Score: 3.309767076331365
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We derive approximation bounds for learning single neuron models using thresholded gradient descent when both the labels and the covariates are possibly corrupted adversarially. We assume the data follows the model $y = \sigma(\mathbf{w}^{*} \cdot \mathbf{x}) + \xi,$ where $\sigma$ is a nonlinear activation function, the noise $\xi$ is Gaussian, and the covariate vector $\mathbf{x}$ is sampled from a sub-Gaussian distribution. We study sigmoidal, leaky-ReLU, and ReLU activation functions and derive a $O(\nu\sqrt{\epsilon\log(1/\epsilon)})$ approximation bound in $\ell_{2}$-norm, with sample complexity $O(d/\epsilon)$ and failure probability $e^{-\Omega(d)}$. We also study the linear regression problem, where $\sigma(\mathbf{x}) = \mathbf{x}$. We derive a $O(\nu\epsilon\log(1/\epsilon))$ approximation bound, improving upon the previous $O(\nu)$ approximation bounds for the gradient-descent based iterative thresholding algorithms of Bhatia et al. (NeurIPS 2015) and Shen and Sanghavi (ICML 2019). Our algorithm has a $O(\textrm{polylog}(N,d)\log(R/\epsilon))$ runtime complexity when $\|\mathbf{w}^{*}\|_2 \leq R$, improving upon the $O(\text{polylog}(N,d)/\epsilon^2)$ runtime complexity of Awasthi et al. (NeurIPS 2022).

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