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\n \n 2025\n \n \n (3)\n \n \n
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\n\n \n \n \n \n \n \n A novel coarsened graph learning method for scalable single-cell data analysis.\n \n \n \n \n\n\n \n Kataria, M.; Srivastava, E.; Arjun, K.; Kumar, S.; Gupta, I.; and Jayadeva\n\n\n \n\n\n\n
Computers in Biology and Medicine, 188: 109873. 2025.\n
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@article{KATARIA2025109873,\r\ntitle = {A novel coarsened graph learning method for scalable single-cell data analysis},\r\njournal = {Computers in Biology and Medicine},\r\nvolume = {188},\r\npages = {109873},\r\nyear = {2025},\r\nissn = {0010-4825},\r\ndoi = {https://doi.org/10.1016/j.compbiomed.2025.109873},\r\nurl = {https://www.sciencedirect.com/science/article/pii/S0010482525002240},\r\nauthor = {Mohit Kataria and Ekta Srivastava and Kumar Arjun and Sandeep Kumar and Ishaan Gupta and Jayadeva}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n CoRE-BOLD: Cross-Domain Robust and Equitable Ensemble for BOLD Signal Analysis.\n \n \n \n\n\n \n Singh, V. K.; Barman, J.; Kumar, S.; and Jayadeva, J.\n\n\n \n\n\n\n In
Machine Learning for Health (ML4H), pages 961–975, 2025. PMLR\n
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@inproceedings{singh2025core,\r\n title={CoRE-BOLD: Cross-Domain Robust and Equitable Ensemble for BOLD Signal Analysis},\r\n author={Singh, Vipul Kumar and Barman, Jyotismita and Kumar, Sandeep and Jayadeva, Jayadeva},\r\n booktitle={Machine Learning for Health (ML4H)},\r\n pages={961--975},\r\n year={2025},\r\n organization={PMLR}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n UGC: Universal Graph Coarsening.\n \n \n \n\n\n \n Kataria, M.; Kumar, S.; and others\n\n\n \n\n\n\n
Advances in Neural Information Processing Systems, 37: 63057–63081. 2025.\n
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@article{kataria2025ugc,\r\n title={UGC: Universal Graph Coarsening},\r\n author={Kataria, Mohit and Kumar, Sandeep and others},\r\n journal={Advances in Neural Information Processing Systems},\r\n volume={37},\r\n pages={63057--63081},\r\n year={2025}\r\n}
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\n \n 2024\n \n \n (2)\n \n \n
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\n\n \n \n \n \n \n No prejudice! fair federated graph neural networks for personalized recommendation.\n \n \n \n\n\n \n Agrawal, N.; Sirohi, A. K.; Kumar, S.; and others\n\n\n \n\n\n\n In
Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 10775–10783, 2024. \n
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@inproceedings{agrawal2024no,\r\n title={No prejudice! fair federated graph neural networks for personalized recommendation},\r\n author={Agrawal, Nimesh and Sirohi, Anuj Kumar and Kumar, Sandeep and others},\r\n booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},\r\n volume={38},\r\n number={10},\r\n pages={10775--10783},\r\n year={2024}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n A Unified Optimization-Based Framework for Certifiably Robust and Fair Graph Neural Networks.\n \n \n \n\n\n \n Singh, V. K.; Kumar, S.; Prasad, A.; and others\n\n\n \n\n\n\n
IEEE Transactions on Signal Processing. 2024.\n
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@article{singh2024unified,\r\n title={A Unified Optimization-Based Framework for Certifiably Robust and Fair Graph Neural Networks},\r\n author={Singh, Vipul Kumar and Kumar, Sandeep and Prasad, Avadhesh and others},\r\n journal={IEEE Transactions on Signal Processing},\r\n year={2024},\r\n publisher={IEEE}\r\n}\r\n\r\n
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\n \n 2023\n \n \n (8)\n \n \n
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\n\n \n \n \n \n \n A unified framework for optimization-based graph coarsening.\n \n \n \n\n\n \n Kumar, M.; Sharma, A.; and Kumar, S.\n\n\n \n\n\n\n
Journal of Machine Learning Research, 24(118): 1–50. 2023.\n
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@article{kumar2023unified,\r\n title={A unified framework for optimization-based graph coarsening},\r\n author={Kumar, Manoj and Sharma, Anurag and Kumar, Sandeep},\r\n journal={Journal of Machine Learning Research},\r\n volume={24},\r\n number={118},\r\n pages={1--50},\r\n year={2023}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Featured graph coarsening with similarity guarantees.\n \n \n \n\n\n \n Kumar, M.; Sharma, A.; Saxena, S.; and Kumar, S.\n\n\n \n\n\n\n In
International Conference on Machine Learning, pages 17953–17975, 2023. PMLR\n
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@inproceedings{kumar2023featured,\r\n title={Featured graph coarsening with similarity guarantees},\r\n author={Kumar, Manoj and Sharma, Anurag and Saxena, Shashwat and Kumar, Sandeep},\r\n booktitle={International Conference on Machine Learning},\r\n pages={17953--17975},\r\n year={2023},\r\n organization={PMLR}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Transition in Human Brain Via Eigenspace of Spatiotemporal Graph.\n \n \n \n\n\n \n Dev, R.; Kumar, S.; and Gandhi, T.\n\n\n \n\n\n\n
IEEE Sensor Letters. 2023.\n
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@article{dev2023transition,\r\n title={Transition in Human Brain Via Eigenspace of Spatiotemporal Graph},\r\n author={Dev, Raghav and Kumar, Sandeep and Gandhi, Tapan},\r\n journal={IEEE Sensor Letters},\r\n year={2023}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Robust and globally sparse Pca via majorization-minimization and variable splitting.\n \n \n \n\n\n \n Brehier, H.; Breloy, A.; El Korso, M. N.; and Kumar, S.\n\n\n \n\n\n\n In
ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5, 2023. IEEE\n
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@inproceedings{brehier2023robust,\r\n title={Robust and globally sparse Pca via majorization-minimization and variable splitting},\r\n author={Brehier, Hugo and Breloy, Arnaud and El Korso, Mohammed Nabil and Kumar, Sandeep},\r\n booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},\r\n pages={1--5},\r\n year={2023},\r\n organization={IEEE}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Does Spatial Location of The Electrodes in EEG Matter for Tracking the Brain States?.\n \n \n \n\n\n \n Dev, R.; Kumar, S.; and Gandhi, T. K.\n\n\n \n\n\n\n In
2023 National Conference on Communications (NCC), pages 1–5, 2023. IEEE\n
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@inproceedings{dev2023does,\r\n title={Does Spatial Location of The Electrodes in EEG Matter for Tracking the Brain States?},\r\n author={Dev, Raghav and Kumar, Sandeep and Gandhi, Tapan Kumar},\r\n booktitle={2023 National Conference on Communications (NCC)},\r\n pages={1--5},\r\n year={2023},\r\n organization={IEEE}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Free Lunch for Privacy Preserving Distributed Graph Learning.\n \n \n \n\n\n \n Agrawal, N.; Malik, N.; and Kumar, S.\n\n\n \n\n\n\n
arXiv preprint arXiv:2305.10869. 2023.\n
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@article{agrawal2023free,\r\n title={Free Lunch for Privacy Preserving Distributed Graph Learning},\r\n author={Agrawal, Nimesh and Malik, Nikita and Kumar, Sandeep},\r\n journal={arXiv preprint arXiv:2305.10869},\r\n year={2023}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Fault detection and isolation of multi-variate time series data using spectral weighted graph auto-encoders.\n \n \n \n\n\n \n Goswami, U.; Rani, J.; Kodamana, H.; Kumar, S.; and Tamboli, P. K.\n\n\n \n\n\n\n
Journal of the Franklin Institute, 360(10): 6783–6803. 2023.\n
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@article{goswami2023fault,\r\n title={Fault detection and isolation of multi-variate time series data using spectral weighted graph auto-encoders},\r\n author={Goswami, Umang and Rani, Jyoti and Kodamana, Hariprasad and Kumar, Sandeep and Tamboli, Prakash Kumar},\r\n journal={Journal of the Franklin Institute},\r\n volume={360},\r\n number={10},\r\n pages={6783--6803},\r\n year={2023},\r\n publisher={Elsevier}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Graph of circuits with GNN for exploring the optimal design space.\n \n \n \n\n\n \n Shahane, A.; Swapna Manjiri, S.; Jain, A.; and Kumar, S.\n\n\n \n\n\n\n
Advances in Neural Information Processing Systems, 36: 6014–6025. 2023.\n
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@article{shahane2023graph,\r\n title={Graph of circuits with GNN for exploring the optimal design space},\r\n author={Shahane, Aditya and Swapna Manjiri, Saripilli and Jain, Ankesh and Kumar, Sandeep},\r\n journal={Advances in Neural Information Processing Systems},\r\n volume={36},\r\n pages={6014--6025},\r\n year={2023}\r\n}\r\n\r\n
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\n \n 2022\n \n \n (2)\n \n \n
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\n\n \n \n \n \n \n Robustifying GNN via weighted laplacian.\n \n \n \n\n\n \n Runwal, B.; Kumar, S.; and others\n\n\n \n\n\n\n In
2022 IEEE International Conference on Signal Processing and Communications (SPCOM), pages 1–5, 2022. IEEE\n
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@inproceedings{runwal2022robustifying,\r\n title={Robustifying GNN via weighted laplacian},\r\n author={Runwal, Bharat and Kumar, Sandeep and others},\r\n booktitle={2022 IEEE International Conference on Signal Processing and Communications (SPCOM)},\r\n pages={1--5},\r\n year={2022},\r\n organization={IEEE}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Robust graph neural networks using weighted graph laplacian.\n \n \n \n\n\n \n Runwal, B.; Kumar, S.; and others\n\n\n \n\n\n\n
arXiv preprint arXiv:2208.01853. 2022.\n
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@article{runwal2022robust,\r\n title={Robust graph neural networks using weighted graph laplacian},\r\n author={Runwal, Bharat and Kumar, Sandeep and others},\r\n journal={arXiv preprint arXiv:2208.01853},\r\n year={2022}\r\n}\r\n\r\n
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\n \n 2021\n \n \n (1)\n \n \n
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\n\n \n \n \n \n \n Majorization-minimization on the Stiefel manifold with application to robust sparse PCA.\n \n \n \n\n\n \n Breloy, A.; Kumar, S.; Sun, Y.; and Palomar, D. P\n\n\n \n\n\n\n
IEEE Transactions on Signal Processing, 69: 1507–1520. 2021.\n
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@article{breloy2021majorization,\r\n title={Majorization-minimization on the Stiefel manifold with application to robust sparse PCA},\r\n author={Breloy, Arnaud and Kumar, Sandeep and Sun, Ying and Palomar, Daniel P},\r\n journal={IEEE Transactions on Signal Processing},\r\n volume={69},\r\n pages={1507--1520},\r\n year={2021},\r\n publisher={IEEE}\r\n}\r\n\r\n
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\n \n 2020\n \n \n (2)\n \n \n
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\n\n \n \n \n \n \n A unified framework for structured graph learning via spectral constraints.\n \n \n \n\n\n \n Kumar, S.; Ying, J.; Cardoso, J. V.; and Palomar, D. P\n\n\n \n\n\n\n
Journal of Machine Learning Research, 21(22): 1–60. 2020.\n
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@article{kumar2020unified,\r\n title={A unified framework for structured graph learning via spectral constraints},\r\n author={Kumar, Sandeep and Ying, Jiaxi and Cardoso, Jos{\\'e} Vin{\\'\\i}cius de M and Palomar, Daniel P},\r\n journal={Journal of Machine Learning Research},\r\n volume={21},\r\n number={22},\r\n pages={1--60},\r\n year={2020}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Student's $ t $ var modeling with missing data via stochastic EM and Gibbs sampling.\n \n \n \n\n\n \n Zhou, R.; Liu, J.; Kumar, S.; and Palomar, D. P\n\n\n \n\n\n\n
IEEE Transactions on Signal Processing, 68: 6198–6211. 2020.\n
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@article{zhou2020student,\r\n title={Student's $ t $ var modeling with missing data via stochastic EM and Gibbs sampling},\r\n author={Zhou, Rui and Liu, Junyan and Kumar, Sandeep and Palomar, Daniel P},\r\n journal={IEEE Transactions on Signal Processing},\r\n volume={68},\r\n pages={6198--6211},\r\n year={2020},\r\n publisher={IEEE}\r\n}\r\n\r\n
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\n \n 2019\n \n \n (7)\n \n \n
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\n\n \n \n \n \n \n Parameter estimation of heavy-tailed AR model with missing data via stochastic EM.\n \n \n \n\n\n \n Liu, J.; Kumar, S.; and Palomar, D. P\n\n\n \n\n\n\n
IEEE Transactions on Signal Processing, 67(8): 2159–2172. 2019.\n
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@article{liu2019parameter,\r\n title={Parameter estimation of heavy-tailed AR model with missing data via stochastic EM},\r\n author={Liu, Junyan and Kumar, Sandeep and Palomar, Daniel P},\r\n journal={IEEE Transactions on Signal Processing},\r\n volume={67},\r\n number={8},\r\n pages={2159--2172},\r\n year={2019},\r\n publisher={IEEE}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Parameter estimation of heavy-tailed AR (p) model from incomplete data.\n \n \n \n\n\n \n Liu, J.; Kumar, S.; and Palomar, D. P\n\n\n \n\n\n\n In
2019 27th European signal processing conference (EUSIPCO), pages 1–5, 2019. IEEE\n
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@inproceedings{liu2019parameter,\r\n title={Parameter estimation of heavy-tailed AR (p) model from incomplete data},\r\n author={Liu, Junyan and Kumar, Sandeep and Palomar, Daniel P},\r\n booktitle={2019 27th European signal processing conference (EUSIPCO)},\r\n pages={1--5},\r\n year={2019},\r\n organization={IEEE}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Incorporating Factor Analysis into Robust Parameters Estimation of High Dimension Data.\n \n \n \n\n\n \n Rui, Z.; Liu, J.; Kumar, S.; and Plaomar, D. P\n\n\n \n\n\n\n
Lecture notes in Computer Science.. 2019.\n
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@article{rui2019incorporating,\r\n title={Incorporating Factor Analysis into Robust Parameters Estimation of High Dimension Data},\r\n author={Rui, Zhou and Liu, Junyan and Kumar, Sandeep and Plaomar, Daniel. P},\r\n journal={Lecture notes in Computer Science.},\r\n year={2019}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Distributed inexact successive convex approximation ADMM: Analysis-part I.\n \n \n \n\n\n \n Kumar, S.; Rajawat, K.; and Palomar, D. P\n\n\n \n\n\n\n
arXiv preprint arXiv:1907.08969. 2019.\n
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@article{kumar2019distributed,\r\n title={Distributed inexact successive convex approximation ADMM: Analysis-part I},\r\n author={Kumar, Sandeep and Rajawat, Ketan and Palomar, Daniel P},\r\n journal={arXiv preprint arXiv:1907.08969},\r\n year={2019}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Optimization algorithms for graph Laplacian estimation via ADMM and MM.\n \n \n \n\n\n \n Zhao, L.; Wang, Y.; Kumar, S.; and Palomar, D. P\n\n\n \n\n\n\n
IEEE Transactions on Signal Processing, 67(16): 4231–4244. 2019.\n
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@article{zhao2019optimization,\r\n title={Optimization algorithms for graph Laplacian estimation via ADMM and MM},\r\n author={Zhao, Licheng and Wang, Yiwei and Kumar, Sandeep and Palomar, Daniel P},\r\n journal={IEEE Transactions on Signal Processing},\r\n volume={67},\r\n number={16},\r\n pages={4231--4244},\r\n year={2019},\r\n publisher={IEEE}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Structured graph learning via Laplacian spectral constraints.\n \n \n \n\n\n \n Kumar, S.; Ying, J.; de Miranda Cardoso, J. V.; and Palomar, D.\n\n\n \n\n\n\n
Advances in neural information processing systems, 32. 2019.\n
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@article{kumar2019structured,\r\n title={Structured graph learning via Laplacian spectral constraints},\r\n author={Kumar, Sandeep and Ying, Jiaxi and de Miranda Cardoso, Jos{\\'e} Vin{\\'\\i}cius and Palomar, Daniel},\r\n journal={Advances in neural information processing systems},\r\n volume={32},\r\n year={2019}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Bipartite structured Gaussian graphical modeling via adjacency spectral priors.\n \n \n \n\n\n \n Kumar, S.; Ying, J.; Cardoso, J. V.; and Palomar, D. P\n\n\n \n\n\n\n In
2019 53rd Asilomar Conference on Signals, Systems, and Computers, pages 322–326, 2019. IEEE\n
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@inproceedings{kumar2019bipartite,\r\n title={Bipartite structured Gaussian graphical modeling via adjacency spectral priors},\r\n author={Kumar, Sandeep and Ying, Jiaxi and Cardoso, Jose Vin{\\'\\i}cius de M and Palomar, Daniel P},\r\n booktitle={2019 53rd Asilomar Conference on Signals, Systems, and Computers},\r\n pages={322--326},\r\n year={2019},\r\n organization={IEEE}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Parameter estimation of heavy-tailed random walk model from incomplete data.\n \n \n \n\n\n \n Liu, J.; Kumar, S.; and Palomar, D. P\n\n\n \n\n\n\n In
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4439–4443, 2018. IEEE\n
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@inproceedings{liu2018parameter,\r\n title={Parameter estimation of heavy-tailed random walk model from incomplete data},\r\n author={Liu, Junyan and Kumar, Sandeep and Palomar, Daniel P},\r\n booktitle={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},\r\n pages={4439--4443},\r\n year={2018},\r\n organization={IEEE}\r\n}\r\n\r\n
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\n \n 2017\n \n \n (2)\n \n \n
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\n\n \n \n \n \n \n Stochastic multidimensional scaling.\n \n \n \n\n\n \n Rajawat, K.; and Kumar, S.\n\n\n \n\n\n\n
IEEE Transactions on Signal and Information Processing over Networks, 3(2): 360–375. 2017.\n
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@article{rajawat2017stochastic,\r\n title={Stochastic multidimensional scaling},\r\n author={Rajawat, Ketan and Kumar, Sandeep},\r\n journal={IEEE Transactions on Signal and Information Processing over Networks},\r\n volume={3},\r\n number={2},\r\n pages={360--375},\r\n year={2017},\r\n publisher={IEEE}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Distributed asynchronous localization over WSNs via non-convex consensus ADMM.\n \n \n \n\n\n \n Kumar, S.; and Rajawat, K.\n\n\n \n\n\n\n In
2017 Twenty-third National Conference on Communications (NCC), pages 1–6, 2017. IEEE\n
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@inproceedings{kumar2017distributed,\r\n title={Distributed asynchronous localization over WSNs via non-convex consensus ADMM},\r\n author={Kumar, Sandeep and Rajawat, Ketan},\r\n booktitle={2017 Twenty-third National Conference on Communications (NCC)},\r\n pages={1--6},\r\n year={2017},\r\n organization={IEEE}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Asynchronous optimization over heterogeneous networks via consensus ADMM.\n \n \n \n\n\n \n Kumar, S.; Jain, R.; and Rajawat, K.\n\n\n \n\n\n\n
IEEE Transactions on Signal and Information Processing over Networks, 3(1): 114–129. 2016.\n
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@article{kumar2016asynchronous,\r\n title={Asynchronous optimization over heterogeneous networks via consensus ADMM},\r\n author={Kumar, Sandeep and Jain, Rahul and Rajawat, Ketan},\r\n journal={IEEE Transactions on Signal and Information Processing over Networks},\r\n volume={3},\r\n number={1},\r\n pages={114--129},\r\n year={2016},\r\n publisher={IEEE}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Cooperative localization of mobile networks via velocity-assisted multidimensional scaling.\n \n \n \n\n\n \n Kumar, S.; Kumar, R.; and Rajawat, K.\n\n\n \n\n\n\n
IEEE Transactions on Signal Processing, 64(7): 1744–1758. 2015.\n
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@article{kumar2015cooperative,\r\n title={Cooperative localization of mobile networks via velocity-assisted multidimensional scaling},\r\n author={Kumar, Sandeep and Kumar, Raju and Rajawat, Ketan},\r\n journal={IEEE Transactions on Signal Processing},\r\n volume={64},\r\n number={7},\r\n pages={1744--1758},\r\n year={2015},\r\n publisher={IEEE}\r\n}\r\n\r\n
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\n\n \n \n \n \n \n Velocity-assisted multidimensional scaling.\n \n \n \n\n\n \n Kumar, S.; and Rajawat, K.\n\n\n \n\n\n\n In
2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pages 570–574, 2015. IEEE\n
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@inproceedings{kumar2015velocity,\r\n title={Velocity-assisted multidimensional scaling},\r\n author={Kumar, Sandeep and Rajawat, Ketan},\r\n booktitle={2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)},\r\n pages={570--574},\r\n year={2015},\r\n organization={IEEE}\r\n}\r\n\r\n
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