Tighter Learning Guarantees on Digital Computers via Concentration of Measure on Finite Spaces

• URL: http://arxiv.org/abs/2402.05576v3
• Date: Fri, 27 Dec 2024 23:45:57 GMT
• Title: Tighter Learning Guarantees on Digital Computers via Concentration of Measure on Finite Spaces
• Authors: Anastasis Kratsios, A. Martina Neuman, Gudmund Pammer,
• Abstract summary: We derive a family of generalization bounds $c_m/N1/ (2vee m)_m=1infty$ tailored for learning models on digital computers.<n>Adjusting the parameter $m$ according to $N$ results in significantly tighter generalization bounds for practical sample sizes $N$.<n>Our family of generalization bounds are formulated based on our new non-asymptotic result for concentration of measure in finite metric spaces.
• Score: 7.373617024876726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning models with inputs in a Euclidean space $\mathbb{R}^d$, when implemented on digital computers, generalize, and their generalization gap converges to $0$ at a rate of $c/N^{1/2}$ concerning the sample size $N$. However, the constant $c>0$ obtained through classical methods can be large in terms of the ambient dimension $d$ and machine precision, posing a challenge when $N$ is small to realistically large. In this paper, we derive a family of generalization bounds $\{c_m/N^{1/(2\vee m)}\}_{m=1}^{\infty}$ tailored for learning models on digital computers, which adapt to both the sample size $N$ and the so-called geometric representation dimension $m$ of the discrete learning problem. Adjusting the parameter $m$ according to $N$ results in significantly tighter generalization bounds for practical sample sizes $N$, while setting $m$ small maintains the optimal dimension-free worst-case rate of $\mathcal{O}(1/N^{1/2})$. Notably, $c_{m}\in \mathcal{O}(m^{1/2})$ for learning models on discretized Euclidean domains. Furthermore, our adaptive generalization bounds are formulated based on our new non-asymptotic result for concentration of measure in finite metric spaces, established via leveraging metric embedding arguments.

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