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Probabilistically Robust Learning: Balancing Average- and Worst-case
  Performance

Probabilistically Robust Learning: Balancing Average- and Worst-case Performance

2 February 2022
Alexander Robey
Luiz F. O. Chamon
George J. Pappas
Hamed Hassani
    AAML
    OOD
ArXivPDFHTML

Papers citing "Probabilistically Robust Learning: Balancing Average- and Worst-case Performance"

13 / 13 papers shown
Title
On Tradeoffs in Learning-Augmented Algorithms
On Tradeoffs in Learning-Augmented Algorithms
Ziyad Benomar
Vianney Perchet
55
1
0
22 Jan 2025
Adversaries With Incentives: A Strategic Alternative to Adversarial Robustness
Adversaries With Incentives: A Strategic Alternative to Adversarial Robustness
Maayan Ehrenberg
Roy Ganz
Nir Rosenfeld
AAML
56
0
0
17 Jun 2024
Fast Computation of Superquantile-Constrained Optimization Through
  Implicit Scenario Reduction
Fast Computation of Superquantile-Constrained Optimization Through Implicit Scenario Reduction
Jake Roth
Ying Cui
26
2
0
13 May 2024
Resilient Constrained Learning
Resilient Constrained Learning
Ignacio Hounie
Alejandro Ribeiro
Luiz F. O. Chamon
23
9
0
04 Jun 2023
Robustness in deep learning: The good (width), the bad (depth), and the
  ugly (initialization)
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)
Zhenyu Zhu
Fanghui Liu
Grigorios G. Chrysos
V. Cevher
39
19
0
15 Sep 2022
Rank-based Decomposable Losses in Machine Learning: A Survey
Rank-based Decomposable Losses in Machine Learning: A Survey
Shu Hu
Xin Wang
Siwei Lyu
35
32
0
18 Jul 2022
How many perturbations break this model? Evaluating robustness beyond
  adversarial accuracy
How many perturbations break this model? Evaluating robustness beyond adversarial accuracy
R. Olivier
Bhiksha Raj
AAML
29
5
0
08 Jul 2022
Adversarial Robustness with Semi-Infinite Constrained Learning
Adversarial Robustness with Semi-Infinite Constrained Learning
Alexander Robey
Luiz F. O. Chamon
George J. Pappas
Hamed Hassani
Alejandro Ribeiro
AAML
OOD
118
42
0
29 Oct 2021
A Survey of Learning Criteria Going Beyond the Usual Risk
A Survey of Learning Criteria Going Beyond the Usual Risk
Matthew J. Holland
Kazuki Tanabe
FaML
24
4
0
11 Oct 2021
On Tilted Losses in Machine Learning: Theory and Applications
On Tilted Losses in Machine Learning: Theory and Applications
Tian Li
Ahmad Beirami
Maziar Sanjabi
Virginia Smith
55
43
0
13 Sep 2021
Consistent Non-Parametric Methods for Maximizing Robustness
Consistent Non-Parametric Methods for Maximizing Robustness
Robi Bhattacharjee
Kamalika Chaudhuri
AAML
39
8
0
18 Feb 2021
RobustBench: a standardized adversarial robustness benchmark
RobustBench: a standardized adversarial robustness benchmark
Francesco Croce
Maksym Andriushchenko
Vikash Sehwag
Edoardo Debenedetti
Nicolas Flammarion
M. Chiang
Prateek Mittal
Matthias Hein
VLM
231
677
0
19 Oct 2020
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach
Yinlam Chow
Aviv Tamar
Shie Mannor
Marco Pavone
67
310
0
06 Jun 2015
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