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FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks.

B. Song, P. Khanduri, X. Zhang, J. Yi, and M. Hong. ICML, volume 202 of Proceedings of Machine Learning Research, page 32304-32330. PMLR, (2023)

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Prashant Sinha

Intermediate states in bivalent ion induced shrinking of polyacrylate coils on surfaces and in solutionP. Sinha. TU Dresden, (2009)

Prashant Duraphe

Prashant Khapane

Prashant Batra

Prashant Gogia

Other publications of authors with the same name

GeoSAM: Fine-tuning SAM with Sparse and Dense Visual Prompting for Automated Segmentation of Mobility Infrastructure.R. Sultan, C. Li, H. Zhu, P. Khanduri, M. Brocanelli, and D. Zhu. CoRR, (2023)Distributed Sequential Hypothesis Testing with Dependent Sensor Observations.S. Zhang, P. Khanduri, and P. Varshney. ACSSC, page 1862-1866. IEEE, (2019)On Random Distortion Testing Based Sequential Non-Parametric Hypothesis Testing*.P. Khanduri, D. Pastor, V. Sharma, and P. Varshney. Allerton, page 328-334. IEEE, (2018)On Distributed Online Convex Optimization with Sublinear Dynamic Regret and Fit.P. Sharma, P. Khanduri, L. Shen, D. Bucci, and P. Varshney. ACSCC, page 1013-1017. IEEE, (2021)Byzantine Resilient Non-Convex SVRG with Distributed Batch Gradient Computations.P. Khanduri, S. Bulusu, P. Sharma, and P. Varshney. CoRR, (2019)INTERACT: achieving low sample and communication complexities in decentralized bilevel learning over networks.Z. Liu, X. Zhang, P. Khanduri, S. Lu, and J. Liu. MobiHoc, page 61-70. ACM, (2022)Anarchic Federated Learning.H. Yang, X. Zhang, P. Khanduri, and J. Liu. ICML, volume 162 of Proceedings of Machine Learning Research, page 25331-25363. PMLR, (2022)Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning.Z. Liu, X. Zhang, P. Khanduri, S. Lu, and J. Liu. ICML, volume 202 of Proceedings of Machine Learning Research, page 22420-22453. PMLR, (2023)An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learning.Y. Zhang, P. Khanduri, I. Tsaknakis, Y. Yao, M. Hong, and S. Liu. CoRR, (2023)Decentralized Learning for Overparameterized Problems: A Multi-Agent Kernel Approximation Approach.P. Khanduri, H. Yang, M. Hong, J. Liu, H. Wai, and S. Liu. ICLR, OpenReview.net, (2022)

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Disambiguation of "Khanduri, Prashant"

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FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks.

Please choose a person to relate this publication to

Prashant Sinha

Prashant Duraphe

Prashant Khapane

Prashant Batra

Prashant Gogia

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Khanduri, Prashant"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks.

Please choose a person to relate this publication to

Prashant Sinha

Prashant Duraphe

Prashant Khapane

Prashant Batra

Prashant Gogia

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks.