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Found 6 papers, 4 papers with code
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization
While there has been progress in developing non-vacuous generalization bounds for deep neural networks, these bounds tend to be uninformative about why deep learning works.
Bayesian Model Selection, the Marginal Likelihood, and Generalization
We provide a partial remedy through a conditional marginal likelihood, which we show is more aligned with generalization, and practically valuable for large-scale hyperparameter learning, such as in deep kernel learning.
Adaptive First- and Second-Order Algorithms for Large-Scale Machine Learning
In this paper, we consider both first- and second-order techniques to address continuous optimization problems arising in machine learning.
Dangers of Bayesian Model Averaging under Covariate Shift
Approximate Bayesian inference for neural networks is considered a robust alternative to standard training, often providing good performance on out-of-distribution data.
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling
In this paper, we show that there are mode-connecting simplicial complexes that form multi-dimensional manifolds of low loss, connecting many independently trained models.
Stochastic Damped L-BFGS with Controlled Norm of the Hessian Approximation
We propose a new stochastic variance-reduced damped L-BFGS algorithm, where we leverage estimates of bounds on the largest and smallest eigenvalues of the Hessian approximation to balance its quality and conditioning.
