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Improving Robust Generalization by Direct PAC-Bayesian Bound
  Minimization

Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization

22 November 2022
Zifa Wang
Nan Ding
Tomer Levinboim
Xi Chen
Radu Soricut
    AAML
ArXivPDFHTML

Papers citing "Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization"

41 / 41 papers shown
Title
Robust Fine-Tuning of Deep Neural Networks with Hessian-based
  Generalization Guarantees
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees
Haotian Ju
Dongyue Li
Hongyang R. Zhang
74
30
0
06 Jun 2022
Focused Adversarial Attacks
Focused Adversarial Attacks
Thomas Cilloni
Charles Walter
Charles Fleming
AAML
37
1
0
19 May 2022
Enhancing Adversarial Training with Second-Order Statistics of Weights
Enhancing Adversarial Training with Second-Order Statistics of Weights
Gao Jin
Xinping Yi
Wei Huang
S. Schewe
Xiaowei Huang
AAML
68
47
0
11 Mar 2022
PACTran: PAC-Bayesian Metrics for Estimating the Transferability of
  Pretrained Models to Classification Tasks
PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Tasks
Nan Ding
Xi Chen
Tomer Levinboim
Soravit Changpinyo
Radu Soricut
54
28
0
10 Mar 2022
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition
Tianyu Pang
Min Lin
Xiao Yang
Junyi Zhu
Shuicheng Yan
100
122
0
21 Feb 2022
Training Certifiably Robust Neural Networks with Efficient Local
  Lipschitz Bounds
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds
Yujia Huang
Huan Zhang
Yuanyuan Shi
J Zico Kolter
Anima Anandkumar
78
78
0
02 Nov 2021
User-friendly introduction to PAC-Bayes bounds
User-friendly introduction to PAC-Bayes bounds
Pierre Alquier
FedML
143
204
0
21 Oct 2021
Improving Robustness using Generated Data
Improving Robustness using Generated Data
Sven Gowal
Sylvestre-Alvise Rebuffi
Olivia Wiles
Florian Stimberg
D. A. Calian
Timothy A. Mann
68
300
0
18 Oct 2021
Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot
  Meta-Learning
Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning
Nan Ding
Xi Chen
Tomer Levinboim
Sebastian Goodman
Radu Soricut
49
33
0
28 May 2021
Robust Learning Meets Generative Models: Can Proxy Distributions Improve
  Adversarial Robustness?
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
Vikash Sehwag
Saeed Mahloujifar
Tinashe Handina
Sihui Dai
Chong Xiang
M. Chiang
Prateek Mittal
OOD
76
130
0
19 Apr 2021
On the Robustness of Vision Transformers to Adversarial Examples
On the Robustness of Vision Transformers to Adversarial Examples
Kaleel Mahmood
Rigel Mahmood
Marten van Dijk
ViT
103
224
0
31 Mar 2021
A PAC-Bayes Analysis of Adversarial Robustness
A PAC-Bayes Analysis of Adversarial Robustness
Paul Viallard
Guillaume Vidot
Amaury Habrard
Emilie Morvant
AAML
29
15
0
19 Feb 2021
Globally-Robust Neural Networks
Globally-Robust Neural Networks
Klas Leino
Zifan Wang
Matt Fredrikson
AAML
OOD
110
130
0
16 Feb 2021
An Image is Worth 16x16 Words: Transformers for Image Recognition at
  Scale
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Alexey Dosovitskiy
Lucas Beyer
Alexander Kolesnikov
Dirk Weissenborn
Xiaohua Zhai
...
Matthias Minderer
G. Heigold
Sylvain Gelly
Jakob Uszkoreit
N. Houlsby
ViT
606
40,961
0
22 Oct 2020
Uncovering the Limits of Adversarial Training against Norm-Bounded
  Adversarial Examples
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples
Sven Gowal
Chongli Qin
J. Uesato
Timothy A. Mann
Pushmeet Kohli
AAML
47
331
0
07 Oct 2020
Sharpness-Aware Minimization for Efficiently Improving Generalization
Sharpness-Aware Minimization for Efficiently Improving Generalization
Pierre Foret
Ariel Kleiner
H. Mobahi
Behnam Neyshabur
AAML
184
1,345
0
03 Oct 2020
Denoising Diffusion Probabilistic Models
Denoising Diffusion Probabilistic Models
Jonathan Ho
Ajay Jain
Pieter Abbeel
DiffM
569
18,008
0
19 Jun 2020
Evading Deepfake-Image Detectors with White- and Black-Box Attacks
Evading Deepfake-Image Detectors with White- and Black-Box Attacks
Nicholas Carlini
Hany Farid
AAML
53
149
0
01 Apr 2020
Reliable evaluation of adversarial robustness with an ensemble of
  diverse parameter-free attacks
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce
Matthias Hein
AAML
213
1,842
0
03 Mar 2020
Overfitting in adversarially robust deep learning
Overfitting in adversarially robust deep learning
Leslie Rice
Eric Wong
Zico Kolter
94
801
0
26 Feb 2020
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
Jonas Rothfuss
Vincent Fortuin
Martin Josifoski
Andreas Krause
UQCV
54
127
0
13 Feb 2020
Information-Theoretic Local Minima Characterization and Regularization
Information-Theoretic Local Minima Characterization and Regularization
Zhiwei Jia
Hao Su
52
19
0
19 Nov 2019
Unlabeled Data Improves Adversarial Robustness
Unlabeled Data Improves Adversarial Robustness
Y. Carmon
Aditi Raghunathan
Ludwig Schmidt
Percy Liang
John C. Duchi
121
752
0
31 May 2019
CutMix: Regularization Strategy to Train Strong Classifiers with
  Localizable Features
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Sangdoo Yun
Dongyoon Han
Seong Joon Oh
Sanghyuk Chun
Junsuk Choe
Y. Yoo
OOD
609
4,777
0
13 May 2019
Negative eigenvalues of the Hessian in deep neural networks
Negative eigenvalues of the Hessian in deep neural networks
Guillaume Alain
Nicolas Le Roux
Pierre-Antoine Manzagol
44
43
0
06 Feb 2019
Theoretically Principled Trade-off between Robustness and Accuracy
Theoretically Principled Trade-off between Robustness and Accuracy
Hongyang R. Zhang
Yaodong Yu
Jiantao Jiao
Eric Xing
L. Ghaoui
Michael I. Jordan
129
2,548
0
24 Jan 2019
MMA Training: Direct Input Space Margin Maximization through Adversarial
  Training
MMA Training: Direct Input Space Margin Maximization through Adversarial Training
G. Ding
Yash Sharma
Kry Yik-Chau Lui
Ruitong Huang
AAML
58
273
0
06 Dec 2018
Transferable Adversarial Attacks for Image and Video Object Detection
Transferable Adversarial Attacks for Image and Video Object Detection
Xingxing Wei
Siyuan Liang
Ning Chen
Xiaochun Cao
AAML
96
223
0
30 Nov 2018
Physical Adversarial Examples for Object Detectors
Physical Adversarial Examples for Object Detectors
Kevin Eykholt
Ivan Evtimov
Earlence Fernandes
Yue Liu
Amir Rahmati
Florian Tramèr
Atul Prakash
Tadayoshi Kohno
D. Song
AAML
92
469
0
20 Jul 2018
Scaling provable adversarial defenses
Scaling provable adversarial defenses
Eric Wong
Frank R. Schmidt
J. H. Metzen
J. Zico Kolter
AAML
76
448
0
31 May 2018
Group Normalization
Group Normalization
Yuxin Wu
Kaiming He
228
3,654
0
22 Mar 2018
Adversarial Logit Pairing
Adversarial Logit Pairing
Harini Kannan
Alexey Kurakin
Ian Goodfellow
AAML
95
628
0
16 Mar 2018
Averaging Weights Leads to Wider Optima and Better Generalization
Averaging Weights Leads to Wider Optima and Better Generalization
Pavel Izmailov
Dmitrii Podoprikhin
T. Garipov
Dmitry Vetrov
A. Wilson
FedML
MoMe
116
1,659
0
14 Mar 2018
mixup: Beyond Empirical Risk Minimization
mixup: Beyond Empirical Risk Minimization
Hongyi Zhang
Moustapha Cissé
Yann N. Dauphin
David Lopez-Paz
NoLa
273
9,760
0
25 Oct 2017
Exploring Generalization in Deep Learning
Exploring Generalization in Deep Learning
Behnam Neyshabur
Srinadh Bhojanapalli
David A. McAllester
Nathan Srebro
FAtt
143
1,255
0
27 Jun 2017
Towards Deep Learning Models Resistant to Adversarial Attacks
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry
Aleksandar Makelov
Ludwig Schmidt
Dimitris Tsipras
Adrian Vladu
SILM
OOD
301
12,063
0
19 Jun 2017
In-Datacenter Performance Analysis of a Tensor Processing Unit
In-Datacenter Performance Analysis of a Tensor Processing Unit
N. Jouppi
C. Young
Nishant Patil
David Patterson
Gaurav Agrawal
...
Vijay Vasudevan
Richard Walter
Walter Wang
Eric Wilcox
Doe Hyun Yoon
233
4,630
0
16 Apr 2017
Towards Evaluating the Robustness of Neural Networks
Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini
D. Wagner
OOD
AAML
258
8,550
0
16 Aug 2016
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
534
5,897
0
08 Jul 2016
PAC-Bayesian Theory Meets Bayesian Inference
PAC-Bayesian Theory Meets Bayesian Inference
Pascal Germain
Francis R. Bach
Alexandre Lacoste
Simon Lacoste-Julien
68
183
0
27 May 2016
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAML
GAN
271
19,045
0
20 Dec 2014
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