Trustworthiness
Recent work has explored the possibility of pruning neural networks at initialization. We assess proposals for doing so: SNIP (Lee et …
The dominant term in PAC-Bayes bounds is often the Kullback–Leibler divergence between the posterior and prior. For so-called …
In suitably initialized wide networks, small learning rates transform deep neural networks (DNNs) into neural tangent kernel (NTK) …
In requiring that a statement of broader impact accompany all submissions for this year’s conference, the NeurIPS program chairs …
Recent work has explored the possibility of pruning neural networks at initialization. We assess proposals for doing so: SNIP (Lee et …
The information-theoretic framework of Russo and J. Zou (2016) and Xu and Raginsky (2017) provides bounds on the generalization error …
We provide a negative resolution to a conjecture of Steinke and Zakynthinou (2020a), by showing that their bound on the conditional …
To date, there has been no formal study of the statistical cost of interpretability in machine learning. As such, the discourse around …
We propose to study the generalization error of a learned predictor ĥ in terms of that of a surrogate (potentially randomized) …
We study whether a neural network optimizes to the same, linearly connected minimum under different samples of SGD noise (e.g., random …