IEEE Computational Intelligence Magazine - May 2023 - 76

[97] Y. Guo, H. Shi, A. Kumar, K. Grauman, T. Rosing,
and R. Feris, " SpotTune: Transfer learning through
adaptive fine-tuning, " in Proc. IEEE/CVF Conf. Comput.
Vis. Pattern Recognit., 2019, pp. 4805-4814.
[98] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton,
" A simple framework for contrastive learning of
visual representations, " in Proc. Int. Conf. Mach. Learn.,
2020, pp. 1597-1607.
[99] K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick,
" Momentum contrast for unsupervised visual representation
learning, " in Proc. IEEE/CVF Conf. Comput.
Vis. Pattern Recognit., 2020, pp. 9729-9738.
[100] X. Chen, H. Fan, R. Girshick, and K. He,
" Improved baselines with momentum contrastive
learning, " 2020, arXiv:2003.04297.
[101] J.-B. Grill et al., " Bootstrap your own latent: A
new approach to self-supervised learning, " in Proc.
Adv. Neural Inf. Process. Syst., 2020, pp. 21271-21284.
[102] X. Chen and K. He, " Exploring simple siamese
representation learning, " in Proc. IEEE/CVF Conf.
Comput. Vis. Pattern Recognit., 2021, pp. 15750-15758.
[103] C. Doersch, A. Gupta, and A. A. Efros, " Unsupervised
visual representation learning by context prediction, "
in Proc. IEEE Int. Conf. Comput. Vis., 2015,
pp. 1422-1430.
[104] N. Komodakis and S. Gidaris, " Unsupervised
representation learning by predicting image rotations, "
in Proc. Int. Conf. Learn. Representations, 2018.
[105] M. Noroozi and P. Favaro, " Unsupervised
learning of visual representations by solving jigsaw
puzzles, " in Proc. 14th Eur. Conf. Comput. Vis., 2016,
pp. 69-84.
[106] G. Larsson, M. Maire, and G. Shakhnarovich,
" Learning representations for automatic colorization, " in
Proc. 14th Eur. Conf. Comput.Vis., 2016, pp. 577-593.
[107] J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T. S.
Huang, " Generative image inpainting with contextual
attention, " in Proc. IEEE Conf. Comput. Vis. Pattern
Recognit., 2018, pp. 5505-5514.
[108] A. N. Carr, Q. Berthet, M. Blondel, O. Teboul,
and N. Zeghidour, " Shuffle to learn: Self-supervised
learning from permutations via differentiable ranking, "
OpenReview, 2020.
[109] T. Mikolov, K. Chen, G. Corrado, and J. Dean,
" Efficient estimation ofword representations in vector
space, " 2013, arXiv:1301.3781.
[110] Y.-S. Ong and A. Gupta, " AIR5:Fivepillars of
artificial intelligence research, " IEEETrans. Emerg. Topics
Comput. Intell.,vol. 3,no. 5,pp.411-415, Oct. 2019.
[111] B. Green and Y. Chen, " Disparate interactions:
An algorithm-in-the-loop analysis of fairness in risk
assessments, " in Proc. Conf. Fairness, Accountability,
Transparency, 2019, pp. 90-99.
[112] I. D. Raji and J. Buolamwini, " Actionable
auditing: Investigating the impact of publicly naming
biased performance results ofcommercial ai products, "
in Proc. AAAI/ACM Conf. AI, Ethics, Soc., 2019,
pp. 429-435.
[113] T. Schnabel, A. Swaminathan, A. Singh, N.
Chandak, and T. Joachims, " Recommendations as
treatments: Debiasing learning and evaluation, " in
Proc. Int. Conf. Mach. Learn., 2016, pp. 1670-1679.
[114] N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman,
and A. Galstyan, " A survey on bias and fairness
in machine learning, " ACM Comput. Surv., vol. 54,
no. 6, pp. 1-35, 2021.
[115] J. Chen, H. Dong, X. Wang, F. Feng, M.
Wang, and X. He, " Bias and debias in recommender
system: A survey and future directions, " 2020,
arXiv:2010.03240.
[116] S. Caton and C. Haas, " Fairness in machine
learning: A survey, " 2020, arXiv:2010.04053.
[117] R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and
C. Dwork, " Learning fair representations, " in Proc.
Int. Conf. Mach. Learn., 2013, pp. 325-333.
[118] M. Hardt, E. Price, and N. Srebro, " Equality of
opportunity in supervised learning, " in Proc. Adv.
Neural Inf. Process. Syst., 2016, pp. 3315-3323.
[119] C. Dwork, M. Hardt, T. Pitassi, O. Reingold,
and R. Zemel, " Fairness through awareness, " in
Proc. 3rd Innov. Theor. Comput. Sci. Conf., 2012,
pp. 214-226.
[120] R. K. Bellamy et al., " AI fairness 360: An
extensible toolkit for detecting, understanding, and
mitigating unwanted algorithmic bias, " 2018,
arXiv:1810.01943.
[121] F. Kamiran and T. Calders, " Data preprocessing
techniques for classification without discrimination, "
Knowl. Inf. Syst., vol. 33, no. 1, pp. 1-33, 2012.
[122] M. Feldman, S. A. Friedler, J. Moeller, C.
Scheidegger, and S. Venkatasubramanian, " Certifying
and removing disparate impact, " in Proc. 21th ACM
SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2015,
pp. 259-268.
[123] H. Jiang and O. Nachum, " Identifying and correcting
label bias in machine learning, " in Proc. Int.
Conf. Artif. Intell. Statist., 2020, pp. 702-712.
[124] D. Xu, Y. Wu, S. Yuan, L. Zhang, and X. Wu,
" Achieving causal fairness through generative adversarial
networks, " in Proc. 28th Int. Joint Conf. Artif.
Intell., 2019, pp. 1452-1458.
[125] M. B. Zafar, I. Valera, M. G. Rogriguez, and K. P.
Gummadi, " Fairness constraints:Mechanisms for fair classification, "
in Proc. Artif. Intell. Statist., 2017, pp. 962-970.
[126] J. Komiyama, A. Takeda, J. Honda, and H. Shimao,
" Nonconvex optimization for regression with
fairness constraints, " in Proc. Int. Conf. Mach. Learn.,
2018, pp. 2737-2746.
[127] I. Valera, A. Singla, and M. Gomez Rodriguez,
" Enhancing the accuracy and fairness of human decision
making, " in Proc. Adv. Neural Inf. Process. Syst.,
vol. 31, 2018.
[128] A. Narayanan, " Translation tutorial: 21 fairness
definitions and their politics, " in Proc. Conf. Fairness
Accountability Transp., vol. 1170, 2018, Art. no. 3.
[129] Y. Wang, X. Wang, A. Beutel, F. Prost, J.
Chen, and E. H. Chi, " Understanding and improving
fairness-accuracy trade-offs in multi-task learning, " in
Proc. 27th ACM SIGKDD Conf. Knowl. Discov. Data
Mining, 2021, pp. 1748-1757.
[130] M. M. Khalili, X. Zhang, M. Abroshan, and S.
Sojoudi, " Improving fairness and privacy in selection
problems, " in Proc. AAAI Conf. Artif. Intell., 2021,
pp. 8092-8100.
[131] Q. Yang, Y. Liu, T. Chen, and Y. Tong, " Federated
machine learning: Concept and applications, "
ACM Trans. Intell. Syst. Technol., vol. 10, no. 2,
pp. 1-19, 2019.
[132] H. Chen, B. An, D. Niyato, Y. C. Soh, and
C. Miao, " Workload factoring and resource sharing
via joint vertical and horizontal cloud federation networks, "
IEEE J. Sel. Areas Commun., vol. 35, no. 3,
pp. 557-570, Mar. 2017.
[133] V. Smith, C.-K. Chiang, M. Sanjabi, and A. Talwalkar,
" Federatedmulti-task learning, " in Proc. 31st Int.
Conf. Neural Inf. Process. Syst., 2017, pp. 4427-4437.
[134] J. Konecny, H. B. McMahan, F. X. Yu, P.
Richtarik, A. T. Suresh, and D. Bacon, " Federated
learning: Strategies for improving communication
efficiency, " 2016, arXiv:1610.05492.
[135] B. McMahan, E. Moore, D. Ramage, S. Hampson,
and B. A. y Arcas, " Communication-efficient learning
of deep networks from decentralized data, " in Proc.
Int. Conf. Artif. Intell., Statist., 2017, pp. 1273-1282.
[136] X. Li, K. Huang, W. Yang, S. Wang, and
Z. Zhang, " On the convergence ofFedAvg on non-IID
data, " in Proc. Int. Conf. Learn. Representations,2019.
[137] N. H. Tran, W. Bao, A. Zomaya, M. N.
Nguyen, and C. S. Hong, " Federated learning over
wireless networks: Optimization model design and
analysis, " in Proc. IEEE Conf. Comput. Commun.,
2019, pp. 1387-1395.
[138] L. Wang, W. Wang, and B. Li, " CMFL: Mitigating
communication overhead for federated learning, "
in Proc. IEEE 39th Int. Conf. Distrib. Comput.
Syst., 2019, pp. 954-964.
[139] S. Hardy et al., " Private federated learning
on vertically partitioned data via entity resolution
and additively homomorphic encryption, " 2017,
arXiv:1711.10677.
[140] S. Yang, B. Ren, X. Zhou, and L. Liu, " Parallel
distributed logistic regression for vertical federated
learning without third-party coordinator, " 2019,
arXiv:1911.09824.
[141] K. Cheng et al., " Secureboost: A lossless federated
learning framework, " IEEE Intell. Syst., vol. 36,
no. 6, pp. 87-98, Nov./Dec. 2021.
[142] Y. Wu, S. Cai, X. Xiao, G. Chen, and B. C.
Ooi, " Privacy preserving vertical federated learning
for tree-based models, " in VLDB Endowment, vol. 13,
no. 11, pp. 2090-2103, 2020.
[143] Q. Li et al., " A survey on federated learning
systems: Vision, hype and reality for data
privacy and protection, " IEEE Trans. Knowl. Data
Eng., early access, Nov. 02, 2021, doi: 10.1109/
TKDE.2021.3124599.
[144] Y. Liu, Y. Kang, C. Xing, T. Chen, and Q. Yang,
" A secure federated transfer learning framework, " IEEE
Intell. Syst., vol. 35, no. 4, pp. 70-82,Jul./Aug. 2020.
[145] X. Peng, Z. Huang, Y. Zhu, and K. Saenko,
" Federated adversarial domain adaptation, " in Proc.
Int. Conf. Learn. Representations,2020.
[146] Q.Liu,C.Chen,J.Qin,Q.Dou, and P.-A.
Heng, " FEDDG: Federated domain generalization
on medical image segmentation via episodic learning
in continuous frequency space, " in Proc.
IEEE/CVF Conf. Comput. Vis. Pattern Recognit.,
2021, pp. 1013-1023.
[147] P. Kairouz et al., " Advances and open problems
in federated learning, " 2019, arXiv:1912.04977.
[148] C. Molnar, Interpretable Machine Learning. Morrisville,
NC, USA:Lulu Press, 2020.
[149] X. Li and B. Liu, " Rule-based classification, " in
Proc. Data Classification: Algorithms Appl., 2013.
[150] J. H. Friedman, " Greedy function approximation:
A gradient boosting machine, " Ann. Statist.,
vol. 29, no. 5, pp. 1189-1232, 2001.
[151] M. T. Ribeiro, S. Singh, and C. Guestrin, "" Why
should I trust you? " Explaining the predictions of any
classifier, " in Proc. 22nd ACM SIGKDD Int. Conf.
Knowl. Discov. DataMining,2016,pp.1135-1144.
[152] M. T. Ribeiro, S. Singh, and C. Guestrin,
" Anchors: High-precision model-agnostic explanations, "
in Proc. AAAI Conf. Artif. Intell., 2018, pp.
1527-1535.
[153] S. Verma, J. Dickerson, and K. Hines, " Counterfactual
explanations for machine learning: A
review, " vol. 32, no. 1, 2020, arXiv:2010.10596.
[154] B. Kim, R. Khanna, and O. O. Koyejo,
" Examples are not enough, learn to criticize! Criticism
for interpretability, " in Proc. Adv. Neural Inf. Process.
Syst., vol. 29, 2016.
[155] P. W. Koh and P. Liang, " Understanding blackbox
predictions via influence functions, " in Proc. Int.
Conf. Mach. Learn., 2017, pp. 1885-1894.
[156] C. Szegedy et al., " Intriguing properties ofneural
networks, " 2013, arXiv:1312.6199.
[157] N. Papernot et al., " Technical report on the
cleverhans v2.1.0 adversarial examples library, " 2018,
arXiv:1610.00768.
[158] F. Croce and M. Hein, " Reliable evaluation of
adversarial robustness with an ensemble of diverse
parameter-free attacks, " in Proc. Int. Conf. Mach.
Learn., 2020, pp. 2206-2216.
[159] N. Carlini and D. Wagner, " Audio adversarial
examples: Targeted attacks on speech-to-text, " in
Proc. IEEE Secur. Privacy Workshops, 2018, pp. 1-7.
[160] E. Wenger et al., "" Hello, it'sme " : Deep learning-based
speech synthesis attacks in the real world, "
2021, arXiv:2109.09598.
[161] P.
Ze_
lasko et al., " Adversarial attacks and
defenses for speech recognition systems, " 2021,
arXiv:2103.17122.
[162] R. Jia and P. Liang, " Adversarial examples for
evaluating reading comprehension systems, " 2017,
arXiv:1707.07328.
[163] H. Xu et al., " Adversarial attacks and defenses in
images, graphs and text: A review, " Int. J. Automat.
Comput., vol. 17, no. 2, pp. 151-178, 2020.
[164] S. Tan, S. Joty, M.-Y. Kan, and R. Socher,
" It's morphin'time! Combating linguistic discrimination
with inflectional perturbations, " 2020,
arXiv:2005.04364.
[165] A. Madry, A. Makelov, L. Schmidt, D. Tsipras,
and A. Vladu, " Towards deep learning models resistant
to adversarial attacks, " 2017, arXiv:1706.06083.
76 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | MAY 2023
http://dx.doi.org/10.1109/TKDE.2021.3124599 http://dx.doi.org/10.1109/TKDE.2021.3124599
IEEE Computational Intelligence Magazine - May 2023

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - May 2023
