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Multi-Task Learning from Summary Statistics: Supplementary Materials A Proofs A.1 Preliminaries
Neural Information Processing Systems
The proofs of Lemmas A.2 and A.3 follow from covering arguments and Bernstein's inequality; refer
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Effective Bayesian Heteroscedastic Regression with Deep Neural Networks Alexander Immer
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Further, we emphasize the significance of principled regularization of the network parameters and prediction.
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Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion
Neural Information Processing Systems
Radar is ubiquitous in autonomous driving systems due to its low cost and good adaptability to bad weather.
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Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates
Neural Information Processing Systems
In particular, we establish the surprising result that: F or any constant learning rate η > 0, the stochastic gradient bandit algorithm is guaranteed to converge to the globally optimal policy almost surely.
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