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Adversarial Risk and Robustness: General Definitions and Implications
  for the Uniform Distribution

Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution

29 October 2018
Dimitrios I. Diochnos
Saeed Mahloujifar
Mohammad Mahmoody
    AAML
ArXivPDFHTML

Papers citing "Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution"

15 / 15 papers shown
Title
Trustworthy Actionable Perturbations
Trustworthy Actionable Perturbations
Jesse Friedbaum
Sudarshan Adiga
Ravi Tandon
AAML
38
2
0
18 May 2024
Attacking Bayes: On the Adversarial Robustness of Bayesian Neural
  Networks
Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks
Yunzhen Feng
Tim G. J. Rudner
Nikolaos Tsilivis
Julia Kempe
AAML
BDL
43
1
0
27 Apr 2024
It Is All About Data: A Survey on the Effects of Data on Adversarial
  Robustness
It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness
Peiyu Xiong
Michael W. Tegegn
Jaskeerat Singh Sarin
Shubhraneel Pal
Julia Rubin
SILM
AAML
37
8
0
17 Mar 2023
Selecting Models based on the Risk of Damage Caused by Adversarial
  Attacks
Selecting Models based on the Risk of Damage Caused by Adversarial Attacks
Jona Klemenc
Holger Trittenbach
AAML
32
1
0
28 Jan 2023
When are Local Queries Useful for Robust Learning?
When are Local Queries Useful for Robust Learning?
Pascale Gourdeau
Varun Kanade
Marta Z. Kwiatkowska
J. Worrell
OOD
40
1
0
12 Oct 2022
Sample Complexity Bounds for Robustly Learning Decision Lists against
  Evasion Attacks
Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks
Pascale Gourdeau
Varun Kanade
Marta Z. Kwiatkowska
J. Worrell
AAML
18
5
0
12 May 2022
Deadwooding: Robust Global Pruning for Deep Neural Networks
Deadwooding: Robust Global Pruning for Deep Neural Networks
Sawinder Kaur
Ferdinando Fioretto
Asif Salekin
27
4
0
10 Feb 2022
The Need for Ethical, Responsible, and Trustworthy Artificial
  Intelligence for Environmental Sciences
The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences
A. McGovern
I. Ebert‐Uphoff
D. Gagne
A. Bostrom
21
64
0
15 Dec 2021
Image classifiers can not be made robust to small perturbations
Image classifiers can not be made robust to small perturbations
Zheng Dai
David K Gifford
VLM
AAML
36
1
0
07 Dec 2021
On the Existence of the Adversarial Bayes Classifier (Extended Version)
On the Existence of the Adversarial Bayes Classifier (Extended Version)
Pranjal Awasthi
Natalie Frank
M. Mohri
31
24
0
03 Dec 2021
Query complexity of adversarial attacks
Query complexity of adversarial attacks
Grzegorz Gluch
R. Urbanke
AAML
27
5
0
02 Oct 2020
More Data Can Expand the Generalization Gap Between Adversarially Robust
  and Standard Models
More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models
Lin Chen
Yifei Min
Mingrui Zhang
Amin Karbasi
OOD
38
64
0
11 Feb 2020
A unified view on differential privacy and robustness to adversarial
  examples
A unified view on differential privacy and robustness to adversarial examples
Rafael Pinot
Florian Yger
Cédric Gouy-Pailler
Jamal Atif
AAML
21
17
0
19 Jun 2019
Lower Bounds for Adversarially Robust PAC Learning
Lower Bounds for Adversarially Robust PAC Learning
Dimitrios I. Diochnos
Saeed Mahloujifar
Mohammad Mahmoody
AAML
27
26
0
13 Jun 2019
Adversarially Robust Learning Could Leverage Computational Hardness
Adversarially Robust Learning Could Leverage Computational Hardness
Sanjam Garg
S. Jha
Saeed Mahloujifar
Mohammad Mahmoody
AAML
23
24
0
28 May 2019
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