Daniel P. Palomar

ORCID

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 102

Top Venues

IEEE Trans. Signal Process.

CoRR

ICASSP

ACSSC

Publications

Jasin Machkour, Arnaud Breloy, Michael Muma, Daniel P. Palomar, Frédéric Pascal
Sparse PCA with False Discovery Rate Controlled Variable Selection. ICASSP (2024)
Amirhossein Javaheri, Arash Amini, Farokh Marvasti, Daniel P. Palomar
Joint Signal Recovery and Graph Learning from Incomplete Time-Series. ICASSP (2024)
Amirhossein Javaheri, Arash Amini, Farokh Marvasti, Daniel P. Palomar
Learning Spatiotemporal Graphical Models From Incomplete Observations. IEEE Trans. Signal Process. 72 (2024)
Andrei Buciulea, Jiaxi Ying, Antonio G. Marques, Daniel P. Palomar
Polynomial Graphical Lasso: Learning Edges from Gaussian Graph-Stationary Signals. CoRR (2024)
Jasin Machkour, Arnaud Breloy, Michael Muma, Daniel P. Palomar, Frédéric Pascal
Sparse PCA with False Discovery Rate Controlled Variable Selection. CoRR (2024)
Jasin Machkour, Daniel P. Palomar, Michael Muma
FDR-Controlled Portfolio Optimization for Sparse Financial Index Tracking. CoRR (2024)
Jasin Machkour, Michael Muma, Daniel P. Palomar
The Informed Elastic Net for Fast Grouped Variable Selection and FDR Control in Genomics Research. CAMSAP (2023)
Amirhossein Javaheri, Arash Amini, Farokh Marvasti, Daniel P. Palomar
Joint Signal Recovery and Graph Learning from Incomplete Time-Series. CoRR (2023)
Xiwen Wang, Rui Zhou, Jiaxi Ying, Daniel P. Palomar
Efficient and Scalable Parametric High-Order Portfolios Design via the Skew-$t$ Distribution. IEEE Trans. Signal Process. 71 (2023)
José Vinícius de Miranda Cardoso, Jiaxi Ying, Sandeep Kumar, Daniel P. Palomar
Estimating Normalized Graph Laplacians in Financial Markets. ICASSP (2023)
Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
Adaptive Estimation of Graphical Models under Total Positivity. ICML (2023)
Jasin Machkour, Michael Muma, Daniel P. Palomar
False Discovery Rate Control for Fast Screening of Large-Scale Genomics Biobanks. SSP (2023)
Xiwen Wang, Jiaxi Ying, Daniel P. Palomar
Gaussian Graphical Models via Bridge-Block Decomposition. CoRR (2023)
Chenyu Gao, Ziping Zhao, Daniel P. Palomar
A Novel Algorithm for GARCH Model Estimation. SSP (2023)
Xiwen Wang, Jiaxi Ying, Daniel P. Palomar
Gaussian Graphical Models via Bridge-Block Decomposition. NeurIPS (2023)
Shengjie Xiu, Daniel P. Palomar
Intraday Volatility-Volume Joint Modeling and Forecasting: A State-Space Approach. EUSIPCO (2023)
Shengjie Xiu, Xiwen Wang, Daniel P. Palomar
A Fast Successive QP Algorithm for General Mean-Variance Portfolio Optimization. IEEE Trans. Signal Process. 71 (2023)
Zepeng Zhang, Ziping Zhao, Kaiming Shen, Daniel P. Palomar, Wei Yu
Discerning and Enhancing the Weighted Sum-Rate Maximization Algorithms in Communications. CoRR (2023)
Esa Ollila, Daniel P. Palomar, Frédéric Pascal
Affine Equivariant Tyler's M-Estimator Applied to Tail Parameter Learning of Elliptical Distributions. IEEE Signal Process. Lett. 30 (2023)
Amirhossein Javaheri, José Vinícius de Miranda Cardoso, Daniel P. Palomar
Graph Learning for Balanced Clustering of Heavy-Tailed Data. CAMSAP (2023)
Jianfeng Cai, José Vinícius de Miranda Cardoso, Daniel P. Palomar, Jiaxi Ying
Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity. NeurIPS (2023)
Xiwen Wang, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
Efficient Algorithms for General Isotone Optimization. AAAI (2022)
José Vinícius de Miranda Cardoso, Jiaxi Ying, Daniel P. Palomar
Learning Bipartite Graphs: Heavy Tails and Multiple Components. NeurIPS (2022)
Rui Zhou, Jiaxi Ying, Daniel P. Palomar
Covariance Matrix Estimation Under Low-Rank Factor Model With Nonnegative Correlations. IEEE Trans. Signal Process. 70 (2022)
Jasin Machkour, Michael Muma, Daniel P. Palomar
False Discovery Rate Control for Grouped Variable Selection in High-Dimensional Linear Models Using the T-Knock Filter. EUSIPCO (2022)
Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
Graphical Models. CoRR (2022)
Rui Zhou, Daniel P. Palomar
Solving High-Order Portfolios via Successive Convex Approximation Algorithms. IEEE Trans. Signal Process. 69 (2021)
Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
A Fast Algorithm for Graph Learning under Attractive Gaussian Markov Random Fields. ACSCC (2021)
Frédéric Pascal, Esa Ollila, Daniel P. Palomar
Improved estimation of the degree of freedom parameter of multivariate $t$-distribution. EUSIPCO (2021)
Jiaxi Ying, José Vinícius de Miranda Cardoso, Jian-Feng Cai, Daniel P. Palomar
Fast Projected Newton-like Method for Precision Matrix Estimation with Nonnegative Partial Correlations. CoRR (2021)
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)
Esa Ollila, Daniel P. Palomar, Frédéric Pascal
Shrinking the Eigenvalues of M-Estimators of Covariance Matrix. 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)
Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
Minimax Estimation of Laplacian Constrained Precision Matrices. AISTATS (2021)
José Vinícius de Miranda Cardoso, Jiaxi Ying, Daniel P. Palomar
Graphical Models in Heavy-Tailed Markets. NeurIPS (2021)
Rui Zhou, Daniel P. Palomar
A Theoretical Basis for Practitioners Heuristic 1/N and Long-Only Quintile Portfolio. ICASSP (2020)
Esa Ollila, Daniel P. Palomar, Frédéric Pascal
M-Estimators of Scatter with Eigenvalue Shrinkage. ICASSP (2020)
Rui Zhou, Daniel P. Palomar
Understanding the Quintile Portfolio. 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. J. Mach. Learn. Res. 21 (2020)
Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model. NeurIPS (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)
Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar
-norm Learn a Sparse Graph under Laplacian Constrained Graphical Models? CoRR (2020)
Linlong Wu, Yiyong Feng, Daniel P. Palomar
General sparse risk parity portfolio design via successive convex optimization. Signal Process. 170 (2020)
José Vinícius de Miranda Cardoso, Daniel P. Palomar
Learning Undirected Graphs in Financial Markets. ACSSC (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)
Ziping Zhao, Daniel P. Palomar
Large-Scale Regularized Portfolio Selection Via Convex Optimization. GlobalSIP (2019)
Ziping Zhao, Rui Zhou, Daniel P. Palomar
Optimal Mean-Reverting Portfolio With Leverage Constraint for Statistical Arbitrage in Finance. IEEE Trans. Signal Process. 67 (7) (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)
Rui Zhou, Ziping Zhao, Daniel P. Palomar
Unified Framework for Minimax MIMO Transmit Beampattern Matching under Waveform Constraints. ICASSP (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)
Linlong Wu, Daniel P. Palomar
Sequence Design for Spectral Shaping via Minimization of Regularized Spectral Level Ratio. IEEE Trans. Signal Process. 67 (18) (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)
Kaiming Shen, Wei Yu, Licheng Zhao, Daniel P. Palomar
Optimization of MIMO Device-to-Device Networks via Matrix Fractional Programming: A Minorization-Maximization Approach. IEEE/ACM Trans. Netw. 27 (5) (2019)
Junyan Liu, Daniel P. Palomar
Regularized robust estimation of mean and covariance matrix for incomplete data. Signal Process. 165 (2019)
Rui Zhou, Daniel P. Palomar
Accelerating the Multivariate SKEW T Parameter Estimation. CAMSAP (2019)
Junyan Liu, Sandeep Kumar, Daniel P. Palomar
Parameter Estimation of Heavy-Tailed Random Walk Model from Incomplete Data. ICASSP (2018)
Ziping Zhao, Daniel P. Palomar
Mean-Reverting Portfolio With Budget Constraint. IEEE Trans. Signal Process. 66 (9) (2018)
Licheng Zhao, Daniel P. Palomar
A Markowitz Portfolio Approach to Options Trading. IEEE Trans. Signal Process. 66 (16) (2018)
Ziping Zhao, Daniel P. Palomar
MIMO Transmit Beampattern Matching Under Waveform Constraints. CoRR (2018)
Ziping Zhao, Songtao Lu, Mingyi Hong, Daniel P. Palomar
Distributed optimization for Generalized Phase Retrieval Over Networks. ACSSC (2018)
Konstantinos Benidis, Yiyong Feng, Daniel P. Palomar
Optimization Methods for Financial Index Tracking: From Theory to Practice. Found. Trends Optim. 3 (3) (2018)
Ziping Zhao, Rui Zhou, Zhongju Wang, Daniel P. Palomar
Optimal Portfolio Design for Statistical Arbitrage in Finance. SSP (2018)
Ziping Zhao, Daniel P. Palomar
MIMO Transmit Beampattern Matching Under Waveform Constraints. ICASSP (2018)
Kaiming Shen, Wei Yu, Licheng Zhao, Daniel P. Palomar
Coordinated Scheduling and Spectrum Sharing via Matrix Fractional Programming. CoRR (2018)
Tianyu Qiu, Xiao Fu, Nicholas D. Sidiropoulos, Daniel P. Palomar
MISO Channel Estimation and Tracking from Received Signal Strength Feedback. IEEE Trans. Signal Process. 66 (7) (2018)
Linlong Wu, Prabhu Babu, Daniel P. Palomar
Transmit Waveform/Receive Filter Design for MIMO Radar With Multiple Waveform Constraints. IEEE Trans. Signal Process. 66 (6) (2018)
Ziping Zhao, Daniel P. Palomar
Sparse Reduced Rank Regression with Nonconvex Regularization. SSP (2018)
Ziping Zhao, Daniel P. Palomar
Sparse Reduced Rank Regression With Nonconvex Regularization. CoRR (2018)
Konstantinos Benidis, Yiyong Feng, Daniel P. Palomar
Sparse Portfolios for High-Dimensional Financial Index Tracking. IEEE Trans. Signal Process. 66 (1) (2018)
Ying Sun, Prabhu Babu, Daniel P. Palomar
Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning. IEEE Trans. Signal Process. 65 (3) (2017)
Javier Rubio, Antonio Pascual-Iserte, Daniel P. Palomar, Andrea Goldsmith
Joint Optimization of Power and Data Transfer in Multiuser MIMO Systems. IEEE Trans. Signal Process. 65 (1) (2017)
Ziping Zhao, Daniel P. Palomar
Robust maximum likelihood estimation of sparse vector error correction model. GlobalSIP (2017)
Licheng Zhao, Daniel P. Palomar
Maximin Joint Optimization of Transmitting Code and Receiving Filter in Radar and Communications. IEEE Trans. Signal Process. 65 (4) (2017)
Licheng Zhao, Junxiao Song, Prabhu Babu, Daniel P. Palomar
A Unified Framework for Low Autocorrelation Sequence Design via Majorization-Minimization. IEEE Trans. Signal Process. 65 (2) (2017)
Ziping Zhao, Daniel P. Palomar
Robust Maximum Likelihood Estimation of Sparse Vector Error Correction Model. CoRR (2017)
Zhongju Wang, Prabhu Babu, Daniel P. Palomar
A low-complexity algorithm for OFDM phase noise estimation. SPAWC (2017)
Zhongju Wang, Prabhu Babu, Daniel P. Palomar
Effective Low-Complexity Optimization Methods for Joint Phase Noise and Channel Estimation in OFDM. IEEE Trans. Signal Process. 65 (12) (2017)
Linlong Wu, Prabhu Babu, Daniel P. Palomar
A fast algorithm for joint design of transmit waveforms and receive filters. SPAWC (2017)
Linlong Wu, Prabhu Babu, Daniel P. Palomar
Cognitive Radar-Based Sequence Design via SINR Maximization. IEEE Trans. Signal Process. 65 (3) (2017)
Junyan Liu, Daniel P. Palomar
Robust estimation of mean and covariance matrix for incomplete data in financial applications. GlobalSIP (2017)
Yiyong Feng, Daniel P. Palomar
A Signal Processing Perspective of Financial Engineering. Found. Trends Signal Process. 9 (1-2) (2016)
Tianyu Qiu, Daniel P. Palomar
Undersampled Phase Retrieval via Majorization-Minimization. CoRR (2016)
Ying Sun, Arnaud Breloy, Prabhu Babu, Daniel P. Palomar, Frédéric Pascal, Guillaume Ginolhac
Low-Complexity Algorithms for Low Rank Clutter Parameters Estimation in Radar Systems. IEEE Trans. Signal Process. 64 (8) (2016)
Junxiao Song, Prabhu Babu, Daniel P. Palomar
Sequence Set Design With Good Correlation Properties Via Majorization-Minimization. IEEE Trans. Signal Process. 64 (11) (2016)
Yang Yang, Marius Pesavento, Mengyi Zhang, Daniel P. Palomar
An Online Parallel Algorithm for Recursive Estimation of Sparse Signals. IEEE Trans. Signal Inf. Process. over Networks 2 (3) (2016)
Ziping Zhao, Daniel P. Palomar
Mean-reverting portfolio design via majorization-minimization method. ACSSC (2016)
Zhongju Wang, Prabhu Babu, Daniel P. Palomar
Optimal design of constant-modulus channel training sequences. ICASSP (2016)
Junxiao Song, Prabhu Babu, Daniel P. Palomar
Sequence Design to Minimize the Weighted Integrated and Peak Sidelobe Levels. IEEE Trans. Signal Process. 64 (8) (2016)
Zhongju Wang, Prabhu Babu, Daniel P. Palomar
Design of PAR-Constrained Sequences for MIMO Channel Estimation via Majorization-Minimization. IEEE Trans. Signal Process. 64 (23) (2016)
Ali N. Akansu, Dmitry Malioutov, Daniel P. Palomar, Emmanuelle Jay, Danilo P. Mandic
Introduction to the Issue on Financial Signal Processing and Machine Learning for Electronic Trading. IEEE J. Sel. Top. Signal Process. 10 (6) (2016)
Javier Rubio, Antonio Pascual-Iserte, Daniel P. Palomar, Andrea Goldsmith
SWIPT techniques for multiuser MIMO broadcast systems. PIMRC (2016)
Ying Sun, Gesualdo Scutari, Daniel P. Palomar
Distributed Nonconvex Multiagent Optimization Over Time-Varying Networks. CoRR (2016)
Yang Yang, Gesualdo Scutari, Daniel P. Palomar, Marius Pesavento
A Parallel Decomposition Method for Nonconvex Stochastic Multi-Agent Optimization Problems. IEEE Trans. Signal Process. 64 (11) (2016)
Zhongju Wang, Prabhu Babu, Daniel P. Palomar
Effective Low-Complexity Optimization Methods for Joint Phase Noise and Channel Estimation in OFDM. CoRR (2016)
Zhongju Wang, Prabhu Babu, Daniel P. Palomar
Design of PAR-Constrained Sequences for MIMO Channel Estimation via Majorization-Minimization. CoRR (2016)

Daniel P. Palomar.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Daniel P. Palomar