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Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices
Santosh Vempala, Andre Wibisono
See Theorem 2 in Section 3.1 formoredetail.
UnderstandingtheGeneralizationBenefitof NormalizationLayers: SharpnessReduction
Previous ablation studies showedthatadding WD to normalized nets indeed leads to better generalization [126, 72, 125].
204da255aea2cd4a75ace6018fad6b4d-Paper.pdf
In this paper, we consider various tree constructions and examine how the choice of parameters affects the generalization error of the resulting random forests as the sample size goes to infinity.
McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds
Rui (Ray) Zhang, Xingwu Liu, Yuyi Wang, Liwei Wang
We consider learning problems in which examples are dependent and their dependency relation is characterized by a graph.
Bayesian Semi-supervised Learning with Graph Gaussian Processes
Yin Cheng Ng, Nicolò Colombo, Ricardo Silva
Neural Information Processing Systems http://nips.cc/
Unsupervised Learning of Object Landmarks through Conditional Image Generation
Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi
Neural Information Processing Systems http://nips.cc/
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards
Alexander Trott, Stephan Zheng, Caiming Xiong, Richard Socher
Neural Information Processing Systems http://nips.cc/