Sandeep Kumar

Publication Activity (10 Years)

Years Active: 2015-2023
Publications (10 Years): 24

Top Topics

Parameter Estimation

Incomplete Data

Heterogeneous Networks

Top Venues

IEEE Trans. Signal Process.

IEEE Trans. Signal Inf. Process. over Networks

Publications

Hugo Brehier, Arnaud Breloy, Mohammed Nabil El Korso, Sandeep Kumar
Robust and Globally Sparse Pca via Majorization-Minimization and Variable Splitting. ICASSP (2023)
José Vinícius de Miranda Cardoso, Jiaxi Ying, Sandeep Kumar, Daniel P. Palomar
Estimating Normalized Graph Laplacians in Financial Markets. ICASSP (2023)
Arnaud Breloy, Sandeep Kumar, Ying Sun, Daniel P. Palomar
Majorization-Minimization on the Stiefel Manifold With Application to Robust Sparse PCA. IEEE Trans. Signal Process. 69 (2021)
Rui Zhou, Junyan Liu, Sandeep Kumar, Daniel P. Palomar
Parameter Estimation for Student's t VAR Model with Missing Data. ICASSP (2021)
Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
A Unified Framework for Structured Graph Learning via Spectral Constraints. J. Mach. Learn. Res. 21 (2020)
Rui Zhou, Junyan Liu, Sandeep Kumar, Daniel P. Palomar
Student's $t$ VAR Modeling With Missing Data Via Stochastic EM and Gibbs Sampling. IEEE Trans. Signal Process. 68 (2020)
Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
A Unified Framework for Structured Graph Learning via Spectral Constraints. CoRR (2019)
Junyan Liu, Sandeep Kumar, Daniel P. Palomar
Parameter Estimation of Heavy-Tailed AR(p) Model from Incomplete Data. EUSIPCO (2019)
Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
Structured Graph Learning Via Laplacian Spectral Constraints. NeurIPS (2019)
Junyan Liu, Sandeep Kumar, Daniel P. Palomar
Parameter Estimation of Heavy-Tailed AR Model With Missing Data Via Stochastic EM. IEEE Trans. Signal Process. 67 (8) (2019)
Rui Zhou, Junyan Liu, Sandeep Kumar, Daniel P. Palomar
Robust Factor Analysis Parameter Estimation. EUROCAST (2) (2019)
Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
Bipartite Structured Gaussian Graphical Modeling via Adjacency Spectral Priors. ACSSC (2019)
Sandeep Kumar, Ketan Rajawat, Daniel P. Palomar
Distributed Inexact Successive Convex Approximation ADMM: Analysis-Part I. CoRR (2019)
Licheng Zhao, Yiwei Wang, Sandeep Kumar, Daniel P. Palomar
Optimization Algorithms for Graph Laplacian Estimation via ADMM and MM. IEEE Trans. Signal Process. 67 (16) (2019)
Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
Structured Graph Learning Via Laplacian Spectral Constraints. CoRR (2019)
Junyan Liu, Sandeep Kumar, Daniel P. Palomar
Parameter Estimation of Heavy-Tailed Random Walk Model from Incomplete Data. ICASSP (2018)
Ketan Rajawat, Sandeep Kumar
Stochastic Multidimensional Scaling. IEEE Trans. Signal Inf. Process. over Networks 3 (2) (2017)
Sandeep Kumar, Rahul Jain, Ketan Rajawat
Asynchronous Optimization Over Heterogeneous Networks Via Consensus ADMM. IEEE Trans. Signal Inf. Process. over Networks 3 (1) (2017)
Sandeep Kumar, Ketan Rajawat
Distributed asynchronous localization over WSNs via non-convex consensus ADMM. NCC (2017)
Ketan Rajawat, Sandeep Kumar
Stochastic Multidimensional Scaling. CoRR (2016)
Sandeep Kumar, Ketan Rajawat
Distributed interference alignment for MIMO cellular network via consensus ADMM. GlobalSIP (2016)
Sandeep Kumar, Raju Kumar, Ketan Rajawat
Cooperative Localization of Mobile Networks Via Velocity-Assisted Multidimensional Scaling. IEEE Trans. Signal Process. 64 (7) (2016)
Sandeep Kumar, Rahul Jain, Ketan Rajawat
Asynchronous Optimization Over Heterogeneous Networks via Consensus ADMM. CoRR (2016)
Sandeep Kumar, Ketan Rajawat
Velocity-assisted multidimensional scaling. SPAWC (2015)