| A Unified Framework for Structured Graph Learning via Spectral Constraints S Kumar, J Ying, JV Cardoso, DP Palomar Journal of Machine Learning Research (JMLR) 21 (22), 1-60, 2020 | 86 | 2020 |
| Asynchronous Optimization over Heterogeneous Networks via Consensus ADMM S Kumar, R Jain, K Rajawat IEEE Transactions on Signal and Information Processing over Networks 3 (1 …, 2017 | 57 | 2017 |
| Structured Graph Learning via Laplacian Spectral Constraints S Kumar, J Ying, JVM Cardoso, D Palomar Advances in Neural Information Processing System (NeurIPS 2019), 2019 | 55 | 2019 |
| Parameter Estimation of Heavy-Tailed AR Model with Missing Data via Stochastic EM J Liu, S Kumar, DP Plaomar IEEE Transactions on Signal Processing 67 (8), 2159-2172, 2019 | 47 | 2019 |
| Optimization Algorithms on Graph Laplacian Estimation via ADMM and MM L Zhao, Y Wang, S Kumar, DP Palomar IEEE Transactions on Signal Processing 67 (16), 4231 - 4244, 2019 | 36 | 2019 |
| Cooperative Localization of Mobile Networks via Velocity-Assisted Multidimensional Scaling Sandeep Kumar, Raju Kumar, and Ketan Rajawat IEEE Transactions on Signal Processing 64 (7), 1744--1758, 2016 | 33* | 2016 |
| Majorization-Minimization on the Stiefel Manifold with Application to Robust Sparse PCA A Berloy, S Kumar, S Ying, D Palomar IEEE Transactions on Signal Processing, 2021 | 16 | 2021 |
| Student’s t VAR Modeling with Missing Data via Stochastic EM and Gibbs Sampling R Zhou, J Liu, S Kumar, DP Palomar IEEE Transactions on Signal Processing, 2020 | 16 | 2020 |
| Stochastic Multidimensional Scaling K Rajawat, S Kumar IEEE Transactions on Signal and Information Processing over Networks, 2017 | 16 | 2017 |
| Parameter Estimation of Heavy-Tailed AR(P) Model from Incomplete Data J Liu, S Kumar, DP Plaomar European Signal Processing Conference, 2019, 2019 | 5 | 2019 |
| Distributed interference alignment for MIMO cellular network via consensus ADMM S Kumar, K Rajawat 2016 IEEE Global Conference on Signal and Information Processing, 2016 | 4 | 2016 |
| Fault detection and isolation of multi-variate time series data using spectral weighted graph auto-encoders U Goswami, J Rani, H Kodamana, S Kumar, PK Tamboli Journal of the Franklin Institute 360 (10), 6783-6803, 2023 | 3 | 2023 |
| Distributed Inexact Successive Convex Approximation ADMM: Analysis-Part I S Kumar, K Rajawat, DP Plaomar Arxiv https://arxiv.org/abs/1907.08969, 2019 | 3 | 2019 |
| Robust graph neural networks using weighted graph laplacian B Runwal, S Kumar arXiv preprint arXiv:2208.01853, 2022 | 2 | 2022 |
| Parameter Estimation of Heavy-Tailed Random Walk Model from Incomplete Data J Liu, S Kumar, DP Plaomar ICASSP, 2018, 2018 | 2 | 2018 |
| A Unified Framework for Optimization-Based Graph Coarsening M Kumar, A Sharma, S Kumar The Journal of Machine Learning Research (JMLR), 2023 | 1 | 2023 |
| Robustifying GNN Via Weighted Laplacian B Runwal, S Kumar 2022 IEEE International Conference on Signal Processing and Communications …, 2022 | 1 | 2022 |
| Bipartite Structured Gaussian Graphical Modelling via Adjacency Spectral Priors S Kumar, J Ying, VDM Cardoso, Jose, D Palomar IEEE Asilomar Conference on Signals, Systems & Computers, 2019 | 1 | 2019 |
| Velocity-assisted multidimensional scaling S Kumar, K Rajawat 2015 IEEE 16th International Workshop on Signal Processing Advances in …, 2015 | 1 | 2015 |
| Robust and Globally Sparse Pca via Majorization-Minimization and Variable Splitting H Brehier, A Breloy, MN El Korso, S Kumar ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | | 2023 |
Daniel P. PalomarDepartment of Electronic & Computer Engineering, Department of Industrial Eng. & Decision AnalyticsVerified email at ust.hk
