Demystify Optimization and Generalization of Over-parameterized
PAC-Bayesian Learning

Demystify Optimization and Generalization of Over-parameterized PAC-Bayesian Learning

4 February 2022

Wei Huang

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Papers citing "Demystify Optimization and Generalization of Over-parameterized PAC-Bayesian Learning"

17 / 17 papers shown

Title
Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective Wuyang Chen Xinyu Gong Zhangyang Wang OOD 109 238 0 23 Feb 2021
PAC-Bayes Bounds for Meta-learning with Data-Dependent Prior Tianyu Liu Jie Lu Zheng Yan Guangquan Zhang 48 14 0 07 Feb 2021
A linearized framework and a new benchmark for model selection for fine-tuning Aditya Deshpande Alessandro Achille Avinash Ravichandran Hao Li Luca Zancato Charless C. Fowlkes Rahul Bhotika Stefano Soatto Pietro Perona ALM 162 48 0 29 Jan 2021
Zero-Cost Proxies for Lightweight NAS Mohamed S. Abdelfattah Abhinav Mehrotra Łukasz Dudziak Nicholas D. Lane 75 260 0 20 Jan 2021
A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks Renjie Liao R. Urtasun R. Zemel 71 90 0 14 Dec 2020
Bayesian Neural Architecture Search using A Training-Free Performance Metric Andrés Camero Hao Wang Enrique Alba Thomas Bäck 56 28 0 29 Jan 2020
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates Jeffrey Negrea Mahdi Haghifam Gintare Karolina Dziugaite Ashish Khisti Daniel M. Roy FedML 172 153 0 06 Nov 2019
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks Yuan Cao Quanquan Gu MLT AI4CE 92 391 0 30 May 2019
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks Gaël Letarte Pascal Germain Benjamin Guedj Franccois Laviolette MQ AI4CE UQCV 73 54 0 24 May 2019
A Primer on PAC-Bayesian Learning Benjamin Guedj 161 223 0 16 Jan 2019
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 227 1,276 0 04 Oct 2018
Non-convex Optimization for Machine Learning Prateek Jain Purushottam Kar 163 486 0 21 Dec 2017
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent Ben London 50 79 0 19 Sep 2017
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks Behnam Neyshabur Srinadh Bhojanapalli Nathan Srebro 83 610 0 29 Jul 2017
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data Gintare Karolina Dziugaite Daniel M. Roy 109 819 0 31 Mar 2017
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis Maxim Raginsky Alexander Rakhlin Matus Telgarsky 73 521 0 13 Feb 2017
Variational Dropout and the Local Reparameterization Trick Diederik P. Kingma Tim Salimans Max Welling BDL 226 1,517 0 08 Jun 2015