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The Dynamics of Functional Diversity throughout Neural Network Training
Deep ensembles offer consistent performance gains, both in terms of reduced generalization error and improved predictive uncertainty …
Pruning Neural Networks at Initialization: Why are We Missing the Mark?
Recent work has explored the possibility of pruning neural networks at initialization. We assess proposals for doing so: SNIP (Lee et …
On the role of data in PAC-Bayes bounds
The dominant term in PAC-Bayes bounds is often the Kullback–Leibler divergence between the posterior and prior. For so-called …
An empirical study of loss landscape geometry and evolution of the data-dependent Neural Tangent Kernel
In suitably initialized wide networks, small learning rates transform deep neural networks (DNNs) into neural tangent kernel (NTK) …
Like A Researcher Stating Broader Impact for the Very First Time
In requiring that a statement of broader impact accompany all submissions for this year’s conference, the NeurIPS program chairs …
Pruning Neural Networks at Initialization: Why Are We Missing the Mark?
Recent work has explored the possibility of pruning neural networks at initialization. We assess proposals for doing so: SNIP (Lee et …
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy-Gradient Iterative Algorithms
The information-theoretic framework of Russo and J. Zou (2016) and Xu and Raginsky (2017) provides bounds on the generalization error …
On the Information Complexity of Proper Learners for VC Classes in the Realizable Case
We provide a negative resolution to a conjecture of Steinke and Zakynthinou (2020a), by showing that their bound on the conditional …
Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability
To date, there has been no formal study of the statistical cost of interpretability in machine learning. As such, the discourse around …
In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors
We propose to study the generalization error of a learned predictor ĥ in terms of that of a surrogate (potentially randomized) …