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Instance-Dependent Generalization Bounds via Optimal Transport

2 November 2022

Papers citing "Instance-Dependent Generalization Bounds via Optimal Transport"

50 / 59 papers shown

Title
Neural Spacetimes for DAG Representation Learning Haitz Sáez de Ocáriz Borde Anastasis Kratsios Marc T. Law Xiaowen Dong Michael Bronstein CML 125 2 0 25 Aug 2024
Adversarial robustness of sparse local Lipschitz predictors Ramchandran Muthukumar Jeremias Sulam AAML 77 13 0 26 Feb 2022
On change of measure inequalities for $f$ -divergences Antoine Picard-Weibel Benjamin Guedj 68 13 0 11 Feb 2022
On the approximation of functions by tanh neural networks Tim De Ryck S. Lanthaler Siddhartha Mishra 53 139 0 18 Apr 2021
The Intrinsic Dimension of Images and Its Impact on Learning Phillip E. Pope Chen Zhu Ahmed Abdelkader Micah Goldblum Tom Goldstein 236 273 0 18 Apr 2021
Robustness to Pruning Predicts Generalization in Deep Neural Networks Lorenz Kuhn Clare Lyle Aidan Gomez Jonas Rothfuss Y. Gal 86 14 0 10 Mar 2021
Failures of model-dependent generalization bounds for least-norm interpolation Peter L. Bartlett Philip M. Long 143 29 0 16 Oct 2020
Neural Rough Differential Equations for Long Time Series James Morrill C. Salvi Patrick Kidger James Foster Terry Lyons AI4TS 73 133 0 17 Sep 2020
Finite-Sample Guarantees for Wasserstein Distributionally Robust Optimization: Breaking the Curse of Dimensionality Rui Gao 72 92 0 09 Sep 2020
On the Generalization Properties of Adversarial Training Yue Xing Qifan Song Guang Cheng AAML 68 34 0 15 Aug 2020
A generative adversarial network approach to calibration of local stochastic volatility models Christa Cuchiero Wahid Khosrawi Josef Teichmann GAN 158 69 0 05 May 2020
Exactly Computing the Local Lipschitz Constant of ReLU Networks Matt Jordan A. Dimakis 70 112 0 02 Mar 2020
A short note on learning discrete distributions C. Canonne 39 68 0 25 Feb 2020
Generalised Lipschitz Regularisation Equals Distributional Robustness Zac Cranko Zhan Shi Xinhua Zhang Richard Nock Simon Kornblith OOD 78 21 0 11 Feb 2020
Fantastic Generalization Measures and Where to Find Them Yiding Jiang Behnam Neyshabur H. Mobahi Dilip Krishnan Samy Bengio AI4CE 145 611 0 04 Dec 2019
An Alternative Probabilistic Interpretation of the Huber Loss Gregory P. Meyer 83 107 0 05 Nov 2019
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks Alexander Levine Soheil Feizi AAML 45 61 0 23 Oct 2019
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning Daniel Kuhn Peyman Mohajerin Esfahani Viet Anh Nguyen Soroosh Shafieezadeh-Abadeh OOD 64 395 0 23 Aug 2019
The phase diagram of approximation rates for deep neural networks Dmitry Yarotsky Anton Zhevnerchuk 76 122 0 22 Jun 2019
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks Mahyar Fazlyab Alexander Robey Hamed Hassani M. Morari George J. Pappas 109 461 0 12 Jun 2019
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers Hadi Salman Greg Yang Jungshian Li Pengchuan Zhang Huan Zhang Ilya P. Razenshteyn Sébastien Bubeck AAML 90 552 0 09 Jun 2019
PAC-Bayes Un-Expected Bernstein Inequality Zakaria Mhammedi Peter Grünwald Benjamin Guedj 63 47 0 31 May 2019
Distributionally Robust Optimization and Generalization in Kernel Methods Matthew Staib Stefanie Jegelka 83 133 0 27 May 2019
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks Mingchen Li Mahdi Soltanolkotabi Samet Oymak NoLa 122 353 0 27 Mar 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 171 2,052 0 08 Feb 2019
Reconciling modern machine learning practice and the bias-variance trade-off M. Belkin Daniel J. Hsu Siyuan Ma Soumik Mandal 247 1,659 0 28 Dec 2018
Sorting out Lipschitz function approximation Cem Anil James Lucas Roger C. Grosse 90 325 0 13 Nov 2018
Predicting the Generalization Gap in Deep Networks with Margin Distributions Yiding Jiang Dilip Krishnan H. Mobahi Samy Bengio UQCV 95 199 0 28 Sep 2018
NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation Anastasis Kratsios Cody B. Hyndman OOD 69 17 0 31 Aug 2018
Neural Ordinary Differential Equations T. Chen Yulia Rubanova J. Bettencourt David Duvenaud AI4CE 450 5,168 0 19 Jun 2018
Implicit Bias of Gradient Descent on Linear Convolutional Networks Suriya Gunasekar Jason D. Lee Daniel Soudry Nathan Srebro MDE 130 414 0 01 Jun 2018
Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach Wenda Zhou Victor Veitch Morgane Austern Ryan P. Adams Peter Orbanz 75 215 0 16 Apr 2018
Regularisation of Neural Networks by Enforcing Lipschitz Continuity Henry Gouk E. Frank Bernhard Pfahringer M. Cree 172 481 0 12 Apr 2018
Stronger generalization bounds for deep nets via a compression approach Sanjeev Arora Rong Ge Behnam Neyshabur Yi Zhang MLT AI4CE 93 643 0 14 Feb 2018
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors Gintare Karolina Dziugaite Daniel M. Roy MLT 77 145 0 26 Dec 2017
Size-Independent Sample Complexity of Neural Networks Noah Golowich Alexander Rakhlin Ohad Shamir 157 551 0 18 Dec 2017
The Implicit Bias of Gradient Descent on Separable Data Daniel Soudry Elad Hoffer Mor Shpigel Nacson Suriya Gunasekar Nathan Srebro 172 924 0 27 Oct 2017
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks Behnam Neyshabur Srinadh Bhojanapalli Nathan Srebro 90 610 0 29 Jul 2017
Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance Jonathan Niles-Weed Francis R. Bach 213 421 0 01 Jul 2017
Exploring Generalization in Deep Learning Behnam Neyshabur Srinadh Bhojanapalli David A. McAllester Nathan Srebro FAtt 162 1,259 0 27 Jun 2017
Spectrally-normalized margin bounds for neural networks Peter L. Bartlett Dylan J. Foster Matus Telgarsky ODL 214 1,225 0 26 Jun 2017
Parseval Networks: Improving Robustness to Adversarial Examples Moustapha Cissé Piotr Bojanowski Edouard Grave Yann N. Dauphin Nicolas Usunier AAML 150 808 0 28 Apr 2017
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data Gintare Karolina Dziugaite Daniel M. Roy 117 820 0 31 Mar 2017
Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks Peter L. Bartlett Nick Harvey Christopher Liaw Abbas Mehrabian 220 434 0 08 Mar 2017
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 351 4,636 0 10 Nov 2016
Simpler PAC-Bayesian Bounds for Hostile Data Pierre Alquier Benjamin Guedj 155 72 0 23 Oct 2016
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 436 2,946 0 15 Sep 2016
Robust Large Margin Deep Neural Networks Jure Sokolić Raja Giryes Guillermo Sapiro M. Rodrigues 76 309 0 26 May 2016
Quantifying Distributional Model Risk via Optimal Transport Jose H. Blanchet Karthyek Murthy 78 426 0 05 Apr 2016
On the properties of variational approximations of Gibbs posteriors Pierre Alquier James Ridgway Nicolas Chopin 109 256 0 12 Jun 2015