Fetching the paper…
Reading the bibliography…
Machine learning has begun to play a central role in many applications.
1908
Earlier work this paper cites.
1908
Earlier work this paper cites.
W. Hoeffding, “Probability inequalities for sums of bounded random variables,” J. American Stat. Assoc. , vol. 58, no. 301, pp. 13–30, 1963
1963
Earlier work this paper cites.
L. Lamport, R. Shostak, and M. Pease, “The Byzantine generals problem,” ACM Trans. Programming Languages and Syst. , vol. 4, no. 3, pp. 382–401, 1982
1982
Earlier work this paper cites.
T. Ypma, “Local convergence of inexact Newton methods,” SIAM Journal on Numerical Analysis , vol. 21, no. 3, pp. 583–590, 1984
1984
Earlier work this paper cites.
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
V. Vapnik, The Nature of Statistical Learning Theory , 2nd ed. New York, NY: Springer-Verlag, 1999
1999
Earlier work this paper cites.
F. Sebastiani, “Machine learning in automated text categorization,” ACM Computing Surveys , vol. 34, no. 1, pp. 1–47, 2002
2002
Earlier work this paper cites.
K. Driscoll, B. Hall, H. Sivencrona, and P. Zumsteq, “Byzantine fault tolerance, from theory to reality,” in Proc. Int. Conf. Computer Safety, Reliability, and Security (SAFECOMP’03) , 2003, pp. 235–248
2003
Earlier work this paper cites.
H. H. Sohrab, Basic Real Analysis , 2nd ed. New York, NY: Springer, 2003
2003
Earlier work this paper cites.
Y. Nesterov, Introductory Lectures on Convex Optimization , ser. Applied optimization; v. 87. Springer US, 2004
2004
Earlier work this paper cites.
P. Dutta, R. Guerraoui, and M. Vukolic, “Best-case complexity of asynchronous Byzantine consensus,” EPFL/IC/200499, Tech. Rep., 2005
2005
Earlier work this paper cites.
J. B. Predd, S. B. Kulkarni, and H. V. Poor, “Distributed learning in wireless sensor networks,” IEEE Signal Process. Mag. , vol. 23, no. 4, pp. 56–69, 2006
2006
Earlier work this paper cites.
S. B. Kotsiantis, I. Zaharakis, and P. Pintelas, “Supervised machine learning: A review of classification techniques,” Emerging Artificial Intell. Applicat. Comput. Eng. , vol. 160, pp. 3–24, 2007
2007
Earlier work this paper cites.
Y. Bengio, “Learning deep architectures for AI,” Found. and Trends Mach. Learning , vol. 2, no. 1, pp. 1–127, 2009
2009
Earlier work this paper cites.
A. Nedic and A. Ozdaglar, “Distributed subgradient methods for multi-agent optimization,” IEEE Trans. Autom. Control , vol. 54, no. 1, pp. 48–61, 2009
2009
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” 2009
2009
Earlier work this paper cites.
S. S. Ram, A. Nedić, and V. Veeravalli, “Distributed stochastic subgradient projection algorithms for convex optimization,” J. Optim. Theory and Appl. , vol. 147, no. 3, pp. 516–545, 2010
2010
Earlier work this paper cites.
P. A. Forero, A. Cano, and G. B. Giannakis, “Consensus-based distributed support vector machines,” J. Mach. Learning Research , vol. 11, pp. 1663–1707, 2010
2010
Earlier work this paper cites.
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Found. and Trends Mach. Learning , vol. 3, no. 1, pp. 1–122, 2011
2011
Earlier work this paper cites.
P. J. Huber, Robust Statistics . Berlin, Heidelberg: Springer, 2011
2011
Earlier work this paper cites.
J. Sousa and A. Bessani, “From Byzantine consensus to BFT state machine replication: A latency-optimal transformation,” in Proc. 9th Euro. Dependable Computing Conf.(EDCC’12) , 2012, pp. 37–48
2012
Earlier work this paper cites.
J. C. Duchi, A. Agarwal, and M. J. Wainwright, “Dual averaging for distributed optimization: Convergence analysis and network scaling,” IEEE Trans. Autom. control , vol. 57, no. 3, pp. 592–606, 2012
2012
Earlier work this paper cites.
J. F. Mota, J. M. Xavier, P. M. Aquiar, and M. Puschel, “D-ADMM: A communication-efficient distributed algorithm for separable optimization,” IEEE Trans. Signal Process. , vol. 61, no. 10, pp. 2718–2723, 2013
2013
Earlier work this paper cites.
H. J. LeBlanc, H. Zhang, X. Koutsoukos, and S. Sundaram, “Resilient asymptotic consensus in robust networks,” IEEE J. Sel. Areas in Commun. , vol. 31, no. 4, pp. 766–781, 2013
2013
Earlier work this paper cites.
A. H. Sayed, “Adaptation, learning, and optimization over networks,” Found. and Trends Mach. Learning , vol. 7, no. 4-5, pp. 311–801, 2014
2014
Earlier work this paper cites.
M. Li, D. G. Andersen, J. W. Park, A. J. Smola, A. Ahmed, V. Josifovski, J. Long, E. J. Shekita, and B.-Y. Su, “Scaling distributed machine learning with the parameter server,” in Proc. 11th USENIX Symp. Operating Systems Design and Implementation (OSDI’14) , Broomfield, CO, Oct. 2014, pp. 583–598
2014
Earlier work this paper cites.
W. Shi, Q. Ling, K. Yuan, G. Wu, and W. Yin, “On the linear convergence of the ADMM in decentralized consensus optimization,” IEEE Trans. Signal Process. , vol. 62, no. 7, pp. 1750–1761, 2014
2014
Earlier work this paper cites.
N. H. Vaidya, L. Tseng, and G. Liang, “Iterative Byzantine vector consensus in incomplete graphs,” in Proc. 15th Int. Conf. Distributed Computing and Networking , 2014, pp. 14–28
2014
Earlier work this paper cites.
A. Nedić and A. Olshevsky, “Distributed optimization over time-varying directed graphs,” IEEE Trans. Autom. Control , vol. 60, no. 3, pp. 601–615, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Cited alongside, same era.
L. Su and N. H. Vaidya, “Fault-tolerant multi-agent optimization: Optimal iterative distributed algorithms,” in Proc. ACM Symp. Principles of Distributed Computing , 2016, pp. 425–434
2016
Cited alongside, same era.
J. Konečný, H. B. McMahan, F. X. Yu, P. Richtarik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” in Proc. NeurIPS Workshop on Private Multi-Party Machine Learning , 2016
2016
Cited alongside, same era.
A. Mokhtari, W. Shi, Q. Ling, and A. Ribeiro, “A decentralized second-order method with exact linear convergence rate for consensus optimization,” IEEE Trans. Signal Inf. Process. Netw. , vol. 2, no. 4, pp. 507–522, 2016
2016
Cited alongside, same era.
L. Li, W. Xu, T. Chen, G. Giannakis, and Q. Ling, “RSA: Byzantine-robust stochastic aggregation methods for distributed learning from heterogeneous datasets,” in Proc. AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 1544–1551
2019
Closest in time.
R. Jin, X. He, and H. Dai, “Distributed Byzantine tolerant stochastic gradient descent in the era of big data,” in Proc. IEEE Intl. Conf. Communications (ICC) , 2019, pp. 1–6
2019
Closest in time.
F. Lin, Q. Ling, and Z. Xiong, “Byzantine-resilient distributed large-scale matrix completion,” in Proc. IEEE Int. Conf. Acoust. Speech and Signal Process. (ICASSP’19) , 2019, pp. 8167–8171
2019
Closest in time.
2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Z. Yang and W. U. Bajwa, “RD-SVM: A resilient distributed support vector machine,” in Proc. IEEE Int. Conf. Acoust. Speech and Signal Process. (ICASSP’16) , 2016, pp. 2444–2448
2016
Cited alongside, same era.
P. D. Lorenzo and G. Scutari, “Next: In-network nonconvex optimization,” IEEE Transactions on Signal and Information Processing over Networks , vol. 2, pp. 120–136, 2016
2016
Cited alongside, same era.
Y. Zhou, H. Zhang, and Y. Liang, “Geometrical properties and accelerated gradient solvers of non-convex phase retrieval,” in Proc. 54th Annu. Allerton Conf. Communication, Control, and Computing , 2016, pp. 331–335
2016
Cited alongside, same era.
L. Su and N. Vaidya, “Multi-agent optimization in the presence of Byzantine adversaries: Fundamental limits,” in Proc. American Control Conference (ACC) , 2016, pp. 7183–7188
2016
Cited alongside, same era.
T. V. J. Le and N. Gopee, “Classifying CIFAR-10 images using unsupervised feature & ensemble learning,” Dec 2016. [Online]. Available: https://trucvietle.me/files/601-report.pdf
2016
Cited alongside, same era.
P. Blanchard, R. Guerraoui, and J. Stainer, “Machine learning with adversaries: Byzantine tolerant gradient descent,” in Proc. Advances in Neural Inf. Process. Syst. , 2017, pp. 118–128
2017
Cited alongside, same era.
A. Mokhtari, Q. Ling, and A. Ribeiro, “Network Newton distributed optimization methods,” IEEE Trans. Signal Process. , vol. 65, no. 1, pp. 146–161, 2017
2017
Cited alongside, same era.
P. Jain and P. Kar, “Non-convex optimization for machine learning,” Foundations and Trends in Machine Learning , vol. 10, no. 3-4, p. 142–336, 2017
2017
Cited alongside, same era.
2019
Closest in time.
2019
Closest in time.
S. Sundaram and B. Gharesifard, “Distributed optimization under adversarial nodes,” IEEE Trans. Autom. Control , vol. 64, no. 3, pp. 1063–1076, 2019
2019
Closest in time.
A. Mitra, J. Richards, S. Bagchi, and S. Sundaram, “Resilient distributed state estimation with mobile agents: Overcoming Byzantine adversaries, communication losses, and intermittent measurements,” Autonomous Robots , vol. 43, no. 3, pp. 743–768, 2019
2019
Closest in time.
Z. Yang and W. U. Bajwa, “ByRDiE: Byzantine-resilient distributed coordinate descent for decentralized learning,” IEEE Trans. Signal Inf. Process. Netw. , vol. 5, no. 4, pp. 611–627, Dec. 2019
2019
Closest in time.
S. Bock and M. Weiß, “A proof of local convergence for the Adam optimizer,” in Proc. International Joint Conference on Neural Networks (IJCNN) . IEEE, 2019, pp. 1–8
2019
Closest in time.
H. Yang, X. zhong Zhang, M. Fang, and J. Liu, “Byzantine-resilient stochastic gradient descent for distributed learning: A Lipschitz-inspired coordinate-wise median approach,” Proc. IEEE Conference on Decision and Control (CDC) , pp. 5832–5837, 2019
2019
Closest in time.
R. M. Golden, Statistical Machine Learning: A Unified Framework . Boca Raton, FL: Chapman and Hall/CRC, 2020
2020
Closest in time.
Z. Yang, A. Gang, and W. U. Bajwa, “Adversary-resilient distributed and decentralized statistical inference and machine learning: An overview of recent advances under the Byzantine threat model,” IEEE Signal Process. Mag. , vol. 37, no. 3, pp. 146–159, May 2020
2020
Closest in time.
M. Nokleby, H. Raja, and W. U. Bajwa, “Scaling-up distributed processing of data streams for machine learning,” Proceedings of the IEEE , vol. 108, no. 11, pp. 1984–2012, 2020
2020
Closest in time.
X. Chen, T. Chen, H. Sun, S. Wu, and M. Hong, “Distributed training with heterogeneous data: Bridging median- and mean-based algorithms,” in Proc. Advances in Neural Information Processing Systems , 2020, pp. 21 616–21 626
2020
Closest in time.
C. Xie, S. Koyejo, and I. Gupta, “Zeno++: Robust fully asynchronous SGD,” in Proc. 37th Intl. Conf. Machine Learning , Jul. 2020, pp. 10 495–10 503
2020
Closest in time.
L. Su and S. Shahrampour, “Finite-time guarantees for Byzantine-resilient distributed state estimation with noisy measurements,” IEEE Transactions on Automatic Control , vol. 65, no. 9, pp. 3758–3771, 2020
2020
Closest in time.
K. Kuwaranancharoen, L. Xin, and S. Sundaram, “Byzantine-resilient distributed optimization of multi-dimensional functions,” in Proc. American Control Conference (ACC) , 2020, pp. 4399–4404
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
H. Sun, S. Lu, and M. Hong, “Improving the sample and communication complexity for decentralized non-convex optimization: Joint gradient estimation and tracking,” in Proc. 37th Intl. Conf. Machine Learning , Jul. 2020, pp. 9217–9228
2020
Closest in time.
2020
Closest in time.
B. Yonel and B. Yazici, “A deterministic theory for exact non-convex phase retrieval,” IEEE Transactions on Signal Processing , vol. 68, pp. 4612–4626, 2020
2020
Closest in time.
2021
Closest in time.
D. Data, L. Song, and S. N. Diggavi, “Data encoding for Byzantine-resilient distributed optimization,” IEEE Transactions on Information Theory , vol. 67, no. 2, pp. 1117–1140, 2021
2021
Closest in time.
S. Pu and A. Nedić, “Distributed stochastic gradient tracking methods,” Mathematical Programming , vol. 187, pp. 409–457, 2021
2021
Closest in time.
J. Peng, W. Li, and Q. Ling, “Byzantine-robust decentralized stochastic optimization over static and time-varying networks,” Signal Processing , vol. 183, p. 108020, 2021
2021
Closest in time.
D. Data and S. Diggavi, “Byzantine-resilient high-dimensional SGD with local iterations on heterogeneous data,” in Proc. 38th Intl. Conf. Machine Learning , Jul. 2021, pp. 2478–2488
2021
Closest in time.
D. Davis and D. Drusvyatskiy, “Graphical convergence of subgradients in nonconvex optimization and learning,” Mathematics of Operations Research , vol. 47, no. 1, pp. 209–231, 2021
2021
Closest in time.