4 citations · 6 across the 17 of their papers we have counts for
3 papers · 1 filter
Generalization Guarantees via Algorithm-dependent Rademacher Complexity
Sarah Sachs, Tim van Erven, Liam Hodgkinson +2
Algorithm- and data-dependent generalization bounds are required to explain the generalization behavior of modern machine learning algorithms. In this context, there exists informa…
mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization
Kayhan Behdin, Qingquan Song, Aman Gupta +7
Modern deep learning models are over-parameterized, where different optima can result in widely varying generalization performance. The Sharpness-Aware Minimization (SAM) technique…
Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers
Liam Hodgkinson, Umut Şimşekli, Rajiv Khanna +1
Despite the ubiquitous use of stochastic optimization algorithms in machine learning, the precise impact of these algorithms and their dynamics on generalization performance in rea…