Enhancing Robust Fairness via Confusional Spectral Regularization | Paper Citation Record

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This paper cites A reductions approach to fair classification.

Enhancing Robust Fairness via Confusional Spectral Regularization A reductions approach to fair classification

Reference 1

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This paper cites Square attack: a query-efficient black-box adversarial attack via random search.

Enhancing Robust Fairness via Confusional Spectral Regularization Square attack: a query-efficient black-box adversarial attack via random search

Reference 2

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This paper cites Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples.

Enhancing Robust Fairness via Confusional Spectral Regularization Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples

Reference 3

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This paper cites The spectral norm of Gaussian matrices with correlated entries.

Enhancing Robust Fairness via Confusional Spectral Regularization The spectral norm of Gaussian matrices with correlated entries

Reference 4

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This paper cites Spectrally-normalized margin bounds for neural networks.

Enhancing Robust Fairness via Confusional Spectral Regularization Spectrally-normalized margin bounds for neural networks

Reference 5

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This paper cites Evasion attacks against machine learning at test time.

Enhancing Robust Fairness via Confusional Spectral Regularization Evasion attacks against machine learning at test time

Reference 6

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This paper cites Towards evaluating the robustness of neural networks.

Enhancing Robust Fairness via Confusional Spectral Regularization Towards evaluating the robustness of neural networks

Reference 7

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This paper cites Minimally distorted adversarial examples with a fast adaptive boundary attack.

Enhancing Robust Fairness via Confusional Spectral Regularization Minimally distorted adversarial examples with a fast adaptive boundary attack

Reference 8

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This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks.

Enhancing Robust Fairness via Confusional Spectral Regularization Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

Reference 9

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This paper cites Learnable boundary guided adversarial training.

Enhancing Robust Fairness via Confusional Spectral Regularization Learnable boundary guided adversarial training

Reference 10

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This paper cites Classes are not equal: An empirical study on image recognition fairness.

Enhancing Robust Fairness via Confusional Spectral Regularization Classes are not equal: An empirical study on image recognition fairness

Reference 11

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This paper cites Class-balanced loss based on effective number of samples.

Enhancing Robust Fairness via Confusional Spectral Regularization Class-balanced loss based on effective number of samples

Reference 12

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This paper cites Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data.

Enhancing Robust Fairness via Confusional Spectral Regularization Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data

Reference 13

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This paper cites Evaluating and Understanding the Robustness of Adversarial Logit Pairing.

Enhancing Robust Fairness via Confusional Spectral Regularization Evaluating and Understanding the Robustness of Adversarial Logit Pairing

Reference 14

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This paper cites Generalizable adversarial training via spectral normalization.

Enhancing Robust Fairness via Confusional Spectral Regularization Generalizable adversarial training via spectral normalization

Reference 15

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This paper cites \"U ber matrizen aus nicht negativen elementen.

Enhancing Robust Fairness via Confusional Spectral Regularization \"U ber matrizen aus nicht negativen elementen

Reference 16

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This paper cites Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm.

Enhancing Robust Fairness via Confusional Spectral Regularization Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm

Reference 17

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This paper cites Goodfellow, Jonathon Shlens, and Christian Szegedy.

Enhancing Robust Fairness via Confusional Spectral Regularization Goodfellow, Jonathon Shlens, and Christian Szegedy

Reference 18

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This paper cites Fairness without demographics in repeated loss minimization.

Enhancing Robust Fairness via Confusional Spectral Regularization Fairness without demographics in repeated loss minimization

Reference 19

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This paper cites Deep residual learning for image recognition.

Enhancing Robust Fairness via Confusional Spectral Regularization Deep residual learning for image recognition

Reference 20

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This paper cites Denoising diffusion probabilistic models.

Enhancing Robust Fairness via Confusional Spectral Regularization Denoising diffusion probabilistic models

Reference 21

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This paper cites On generalization of graph autoencoders with adversarial training.

Enhancing Robust Fairness via Confusional Spectral Regularization On generalization of graph autoencoders with adversarial training

Reference 22

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This paper cites Enhancing adversarial training via reweighting optimization trajectory.

Enhancing Robust Fairness via Confusional Spectral Regularization Enhancing adversarial training via reweighting optimization trajectory

Reference 23

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This paper cites Fantastic generalization measures and where to find them.

Enhancing Robust Fairness via Confusional Spectral Regularization Fantastic generalization measures and where to find them

Reference 24

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This paper cites How does weight correlation affect the generalisation ability of deep neural networks.

Enhancing Robust Fairness via Confusional Spectral Regularization How does weight correlation affect the generalisation ability of deep neural networks

Reference 25

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This paper cites Enhancing adversarial training with second-order statistics of weights.

Enhancing Robust Fairness via Confusional Spectral Regularization Enhancing adversarial training with second-order statistics of weights

Reference 26

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This paper cites Weight Expansion: A New Perspective on Dropout and Generalization.

Enhancing Robust Fairness via Confusional Spectral Regularization Weight Expansion: A New Perspective on Dropout and Generalization

Reference 27

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This paper cites Adversarial Logit Pairing.

Enhancing Robust Fairness via Confusional Spectral Regularization Adversarial Logit Pairing

Reference 28

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This paper cites Pac-bayes bounds for the risk of the majority vote and the variance of the gibbs classifier.

Enhancing Robust Fairness via Confusional Spectral Regularization Pac-bayes bounds for the risk of the majority vote and the variance of the gibbs classifier

Reference 29

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This paper cites (not) bounding the true error.

Enhancing Robust Fairness via Confusional Spectral Regularization (not) bounding the true error

Reference 30

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This paper cites Pac-bayes risk bounds for sample-compressed gibbs classifiers.

Enhancing Robust Fairness via Confusional Spectral Regularization Pac-bayes risk bounds for sample-compressed gibbs classifiers

Reference 31

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This paper cites Adversarial vertex mixup: Toward better adversarially robust generalization.

Enhancing Robust Fairness via Confusional Spectral Regularization Adversarial vertex mixup: Toward better adversarially robust generalization

Reference 32

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Enhancing Robust Fairness via Confusional Spectral Regularization Wat: improve the worst-class robustness in adversarial training

Reference 33

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This paper cites Out-of-bounding-box triggers: A stealthy approach to cheat object detectors.

Enhancing Robust Fairness via Confusional Spectral Regularization Out-of-bounding-box triggers: A stealthy approach to cheat object detectors

Reference 34

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This paper cites Just train twice: Improving group robustness without training group information.

Enhancing Robust Fairness via Confusional Spectral Regularization Just train twice: Improving group robustness without training group information

Reference 35

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This paper cites Large-scale long-tailed recognition in an open world.

Enhancing Robust Fairness via Confusional Spectral Regularization Large-scale long-tailed recognition in an open world

Reference 36

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This paper cites On the tradeoff between robustness and fairness.

Enhancing Robust Fairness via Confusional Spectral Regularization On the tradeoff between robustness and fairness

Reference 37

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This paper cites Towards deep learning models resistant to adversarial attacks.

Enhancing Robust Fairness via Confusional Spectral Regularization Towards deep learning models resistant to adversarial attacks

Reference 38

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Enhancing Robust Fairness via Confusional Spectral Regularization Simplified pac-bayesian margin bounds

Reference 39

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Enhancing Robust Fairness via Confusional Spectral Regularization Pac-bayesian model averaging

Reference 40

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This paper cites Long-tail learning via logit adjustment.

Enhancing Robust Fairness via Confusional Spectral Regularization Long-tail learning via logit adjustment

Reference 41

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Enhancing Robust Fairness via Confusional Spectral Regularization Pac-bayesian generalization bound on confusion matrix for multi-class classification

Reference 42

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This paper cites Learning from failure: De-biasing classifier from biased classifier.

Enhancing Robust Fairness via Confusional Spectral Regularization Learning from failure: De-biasing classifier from biased classifier

Reference 43

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This paper cites Exploring Generalization in Deep Learning.

Enhancing Robust Fairness via Confusional Spectral Regularization Exploring Generalization in Deep Learning

Reference 44

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This paper cites A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks.

Enhancing Robust Fairness via Confusional Spectral Regularization A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks

Reference 45

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This paper cites Robustness and accuracy could be reconcilable by (proper) definition.

Enhancing Robust Fairness via Confusional Spectral Regularization Robustness and accuracy could be reconcilable by (proper) definition

Reference 46

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This paper cites Improving robust fariness via balance adversarial training.

Enhancing Robust Fairness via Confusional Spectral Regularization Improving robust fariness via balance adversarial training

Reference 47

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This paper cites Intriguing properties of neural networks.

Enhancing Robust Fairness via Confusional Spectral Regularization Intriguing properties of neural networks

Reference 48

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This paper cites Better diffusion models further improve adversarial training.

Enhancing Robust Fairness via Confusional Spectral Regularization Better diffusion models further improve adversarial training

Reference 49

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This paper cites Cfa: Class-wise calibrated fair adversarial training.

Enhancing Robust Fairness via Confusional Spectral Regularization Cfa: Class-wise calibrated fair adversarial training

Reference 50

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This paper cites Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets.

Enhancing Robust Fairness via Confusional Spectral Regularization Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets

Reference 51

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This paper cites Adversarial weight perturbation helps robust generalization.

Enhancing Robust Fairness via Confusional Spectral Regularization Adversarial weight perturbation helps robust generalization

Reference 52

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This paper cites Pac-bayesian spectrally-normalized bounds for adversarially robust generalization.

Enhancing Robust Fairness via Confusional Spectral Regularization Pac-bayesian spectrally-normalized bounds for adversarially robust generalization

Reference 53

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This paper cites To be robust or to be fair: Towards fairness in adversarial training.

Enhancing Robust Fairness via Confusional Spectral Regularization To be robust or to be fair: Towards fairness in adversarial training

Reference 54

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This paper cites Spectral Norm Regularization for Improving the Generalizability of Deep Learning.

Enhancing Robust Fairness via Confusional Spectral Regularization Spectral Norm Regularization for Improving the Generalizability of Deep Learning

Reference 55

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This paper cites Wide residual networks.

Enhancing Robust Fairness via Confusional Spectral Regularization Wide residual networks

Reference 56

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This paper cites Theoretically principled trade-off between robustness and accuracy.

Enhancing Robust Fairness via Confusional Spectral Regularization Theoretically principled trade-off between robustness and accuracy

Reference 57

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This paper cites Trajpac: Towards robustness verification of pedestrian trajectory prediction models.

Enhancing Robust Fairness via Confusional Spectral Regularization Trajpac: Towards robustness verification of pedestrian trajectory prediction models

Reference 58

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This paper cites How Does Mixup Help With Robustness and Generalization?.

Enhancing Robust Fairness via Confusional Spectral Regularization How Does Mixup Help With Robustness and Generalization?

Reference 59

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This paper cites Towards Fairness-Aware Adversarial Learning.

Enhancing Robust Fairness via Confusional Spectral Regularization Towards Fairness-Aware Adversarial Learning

Reference 60

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Enhancing Robust Fairness via Confusional Spectral Regularization write newline

Reference 61

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This paper cites @esa (Ref.

Enhancing Robust Fairness via Confusional Spectral Regularization @esa (Ref

Reference 62

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Enhancing Robust Fairness via Confusional Spectral Regularization Unresolved cited work

Reference 63

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Enhancing Robust Fairness via Confusional Spectral Regularization Unresolved cited work

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