uniform-stability

This paper presents a new logarithmic-free upper bound for the generalization gap in uniformly stable algorithms and constructs a deterministic learning problem that achieves optimal high-probability dependence, closing a gap in the literature.

This paper analyzes generalization error, uniform stability, and uniform argument stability of gradient descent (GD) and stochastic gradient descent (SGD) over discrete parameter spaces with deterministic or stochastic rounding, showing that rounding degrades generalization for GD and introduces dimension-dependent errors for stochastic rounding.