An Improved Uniform Convergence Bound with Fat-Shattering Dimension
arXiv:2307.06644v13 citationsh-index: 10
Originality Incremental advance
AI Analysis
This addresses a theoretical problem in statistical learning theory for researchers, but it is incremental as it refines existing bounds.
The paper tackled the gap between upper and lower bounds on sample complexity for uniform convergence of real-valued functions, providing an improved bound that closes this open gap.
The fat-shattering dimension characterizes the uniform convergence property of real-valued functions. The state-of-the-art upper bounds feature a multiplicative squared logarithmic factor on the sample complexity, leaving an open gap with the existing lower bound. We provide an improved uniform convergence bound that closes this gap.