254 citations · 1.3k across the 50 of their papers we have counts for
9 papers · 1 filter
NeurIPS 2020 Competition: Predicting Generalization in Deep Learning
Yiding Jiang, Pierre Foret, Scott Yak +7
Understanding generalization in deep learning is arguably one of the most important questions in deep learning. Deep learning has been successfully adopted to a large number of pro…
On the Information Complexity of Proper Learners for VC Classes in the Realizable Case
Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran +1
We provide a negative resolution to a conjecture of Steinke and Zakynthinou (2020a), by showing that their bound on the conditional mutual information (CMI) of proper learners of V…
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel
Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul +3
In suitably initialized wide networks, small learning rates transform deep neural networks (DNNs) into neural tangent kernel (NTK) machines, whose training dynamics is well-approxi…
Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability
Gintare Karolina Dziugaite, Shai Ben-David, Daniel M. Roy
To date, there has been no formal study of the statistical cost of interpretability in machine learning. As such, the discourse around potential trade-offs is often informal and mi…
In Search of Robust Measures of Generalization
Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal +5
One of the principal scientific challenges in deep learning is explaining generalization, i.e., why the particular way the community now trains networks to achieve small training e…
Pruning Neural Networks at Initialization: Why are We Missing the Mark?
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy +1
Recent work has explored the possibility of pruning neural networks at initialization. We assess proposals for doing so: SNIP (Lee et al., 2019), GraSP (Wang et al., 2020), SynFlow…