Statistical Learning
A Regret-V ariance Trade-Off in Online Learning
Neural Information Processing Systems
We prove that a variant of EW A either achieves a negative regret (i.e., the algorithm
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Quasi-Bayes meets Vines
Neural Information Processing Systems
QB construction for the one-dimensional marginals and model the dependence using highly expressive vine copulas.
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On the Double Descent of Random Features Models Trained with SGD
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We study generalization properties of random features (RF) regression in high dimensions optimized by stochastic gradient descent (SGD) in under-/over-parameterized regime.
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Globally Gated Deep Linear Networks
Neural Information Processing Systems
Interestingly, networks with a large number of gating units behave similarly to standard ReLU architectures.
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