Publications

2025

X. Luo, S. Bai, B. Li, P. Drineas, R. Zhang, and B. Bullins, Stacey: Promoting Stochastic Steepest Descent via Accelerated \ell_p-Smooth Nonconvex Optimization, International Conference on Machine Learning (ICML), accepted, 2025.
G. Dexter, C. Boutsikas, L. Ma, I. C.F. Ipsen, and P. Drineas, Stochastic Rounding Implicitly Regularizes Tall-and-Thin Matrices, SIAM Journal on Matrix Analysis and Applications (SIMAX), 46(1), 10.1137/24M164767, pp. 341-369, 2025.
Y. Jin, A. Topaloudi, S. Shekhar, A. N. Scott, B.D. Colon, P. Drineas, C. Rochet, and P. Paschou, Neuropathology-based GWAS for Alzheimer’s disease reveals novel susceptibility loci and highlights sex-specific pathways, Acta Neuropathologica Communications, 13(1), pp. 1-18, 2025.

2024

G. Dexter, P. Drineas, and R. Khanna, The space complexity of approximating logistic loss, Neural Information Processing Systems (NeurIPS), 2024.
P. Drineas and I. C.F. Ipsen, Stochastic Rounding 2.0, With a View Towards Complexity Analysis, SIAM News, Nov 2024.
M. Burch, A. Bose, G. Dexter, L. Parida, and P. Drineas, Matrix sketching for linear mixed models in association studies, Genome Research (34), pp. 1304-1311, 2024.
M. Chung, J. Carlos De los Reyes, P. Drineas, R. Renaut, and A. Townsend, Meeting in the Middle for RandNLA, Optimization, and Inverse Problems, SIAM News, Oct 2024.
S. Fadnavis, A. Chowdhury, J. Batson, P. Drineas, and E. Garyfallidis, Patch2Self2: Self-supervised Denoising on Coresets via Matrix Sketching, Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
C. Boutsikas, P. Drineas, and I. C.F. Ipsen, Small singular values can increase in lower precision, SIAM Journal on Matrix Analysis and Applications (SIMAX), 45(3), 10.1137/23M1557209, 2024.
M. Burch, A. Bose, G. Dexter, L. Parida, and P. Drineas, MaSk-LMM: A Matrix Sketching Framework for Linear Mixed Models in Association Studies, International Conference on Research in Computational Molecular Biology (RECOMB), pp. 352-355, 2024.
R. Bhattacharjee, G. Dexter, P. Drineas, C. Musco, and A. Ray, Sublinear Time Eigenvalue Approximation via Random Sampling, Algorithmica (86), pp. 1764–1829, 2024.
S. Shumeli, P. Drineas, and H. Avron, Low-Rank Updates of Matrix Square Roots, Numerical Linear Algebra and Applications, 31(1), doi/10.1002/nla.2528, pp. 1-15, 2024.

2023

G. Dexter, P. Drineas, D. Woodruff, and T. Yasuda, Sketching Algorithms for Sparse Dictionary Learning: PTAS and Turnstile Streaming, Neural Information Processing Systems (NeurIPS), 2023.
C. Boutsikas, P. Drineas, M. Mertzanidis, A. Psomas, and P. Verma, Refined Mechanism Design for Approximately Structured Priors via Active Regression, Neural Information Processing Systems (NeurIPS), 2023.
P. Jain, M. Yates, C. Rubin de Celis, P. Drineas, N. Jahanshad, P. Thompson, and P Paschou, Multiomic approach and Mendelian randomization analysis identify causal associations between blood biomarkers and subcortical brain structure volumes, NeuroImage, 284, 120466, 2023.
P. R. Jain, M. Burch, M. Martinez, P. Mir, J. P. Fichna, C. Zekanowski, R. Rizzo, Z. Tumer, C. Barta, E. Yannaki, J. Stamatoyannopoulos, P. Drineas, and P. Paschou, Can polygenic risk scores help explain disease prevalence differences around the world? A worldwide investigation, BMC Genomic Data, 24(1), 70, 2023.
A. Bose, M. Burch, A. Chowdhury, P. Paschou, and P. Drineas, Structure-informed clustering for population stratification in association studies, BMC Bioinformatics 24, 411, 2023.
A. Topaloudi, P. Jain, M. B. Martinez, J. K. Bryant, G. Reynolds, Z. Zagoriti, G. Lagoumintzis, E. Zamba-Papanicolaou, J. Tzartos, K. Poulas, K. A. Kleopa, S. J. Tzartos, M. Georgitsi, P. Drineas, and P. Paschou, PheWAS and cross-disorder analysis reveal genetic architecture, pleiotropic loci and phenotypic correlations across 11 autoimmune disorders, Frontiers in Immunology, Volume 14, 10.3389/fimmu.2023.1147573, 2023.
R. Bhattacharjee, G. Dexter, P. Drineas, C. Musco, and A. Ray, Sublinear Time Eigenvalue Approximation via Random Sampling, 50th EATCS International Colloquium on Automata, Languages and Programming (ICALP), pp. 21:1-21:18, 2023.
V. Georgiou, C. Boutsikas, P. Drineas, and H. Anz, A Mixed Precision Randomized Preconditioner for the LSQR Solver on GPUs, ISC High Performance, pp. 164-181, 2023.
F. Tsetsos, A. Topaloudi,..., P. Drineas, A. Dietrich, L. K. Davis, J. J. Crowley, C. A. Mathews, J. M. Scharf, M. Georgitsi, P. J. Hoekstra, and P. Paschou, Genome-wide Association Study points to novel locus for Gilles de la Tourette Syndrome, Biological Psychiatry, https://doi.org/10.1016/j.biopsych.2023.01.023, 2023.
P. Jain, T. Miller-Fleming, A. Topaloudi, D. Yu, P. Drineas,..., L. K. Davis, and P. Paschou, Polygenic risk score-based phenome-wide association study identifies novel associations for Tourette syndrome, Translational Psychiatry, 13(1):69, doi: 10.1038/s41398-023-02341-5, 2023.

2022

A. Chowdhury, G. Dexter, P. London, H. Avron, and P. Drineas, Faster Randomized Interior Point Methods for Tall/Wide Linear Programs, Journal of Machine Learning Research (JMLR), 23(336):1−48, 2022.
G. Dexter, A. Chowdhury, H. Avron, and P. Drineas, On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming, International Conference on Machine Learning (ICML), 2022. Selected for long presentation (2% acceptance rate).
A. Chowdhury, A. Bose, S. Zhou, D. Woodruff, and P. Drineas, A Fast, Provably Accurate Approximation Algorithm for Sparse Principal Component Analysis Reveals Human Genetic Variation Across the World, International Conference on Research in Computational Molecular Biology (RECOMB), 2022.
Z. Yang, P. Paschou, and P. Drineas, Reconstructing SNP Allele and Genotype Frequencies from GWAS Summary Statistics, Scientific Reports, vol. 12 (8242), 2022.
P. Paschou, Y. Jin, K. Müller-Vahl,...,P. Drineas,...,J. Buitelaar, B. Franke, O. Van Den Heuvel, N. Jahanshad, P. M. Thompson, and K. J. Black, Enhancing neuroimaging genetics through meta-analysis for Tourette syndrome (ENIGMA-TS): A worldwide platform for collaboration, Frontiers in Psychiatry, 13:958688, doi: 10.3389/fpsyt.2022.958688, 2022.

2021

V. Ravindra, P. Drineas, A. Grama, Constructing Compact Signatures for Individual Fingerprinting of Brain Connectomes, Frontiers in Neuroscience, doi.org/10.3389/fnins.2021.549322, 2021.
A. Bose, D. E. Platt, L. Parida, P. Drineas, and P. Paschou, Integrating linguistics, social structure, and geography to model genetic diversity within India, Molecular Biology and Evolution, doi.org/10.1093/molbev/msaa321, 2021.

2020

A. Chowdhuri, P. London, H. Avron, and P. Drineas, Speeding up Linear Programming using Randomized Linear Algebra, 34th Conference on Neural Information Processing Systems (NeurIPS), 2020.
V. Braverman, P. Drineas, C. Musco, C. Musco, J. Upadhyay, D. Woodruff, and S. Zhou, Numerical Linear Algebra in the Sliding Window Model, Proc. of the 61st IEEE Symposium on Foundations of Computer Science (FOCS), 2020.
E. Kontopoulou, G. Dexter, W. Szpankowski, A. Grama, and P. Drineas, Randomized Linear Algebra Approaches to Estimate the Von Neumann Entropy of Density Matrices, IEEE Transactions on Information Theory, 6(8), pp. 5003-5021, 2020.
A. Bose, M. C. Burch, A. Chowdhury, P. Paschou, and P. Drineas, CluStrat: a structure informed clustering strategy for population stratification, International Conference on Research in Computational Molecular Biology (RECOMB), Lecture Notes in Computer Science (volume 12074), 2020.
A. Chowdhury, P. Drineas, D. P. Woodruff, and S. Zhou, Approximation Algorithms for Sparse Principal Component Analysis, 2020.

2019

A. Chowdhuri, J. Yang, and P. Drineas, Randomized Iterative Algorithms for Fisher Discriminant Analysis, Conference on Uncertainty in Artificial Intelligence (UAI), 64, 2019. Selected for oral presentation.
P. Drineas, F. Tsetsos, A. Plantinga, I. Lazaridis, E. Yannaki, A. Razou, K. Kanaki, M. Michalodimitrakis, F. Perez-Jimenez, G. De Silvestro, M. C. Renda, J. A. Stamatoyannopoulos, K. K. Kidd, B. L. Browning, P. Paschou, G. Stamatoyannopoulos, Genetic History of the Population of Crete, Annals of Human Genetics, https://doi.org/10.1111/ahg.12328, pp. 1-16, 2019.
A. Bose, V. Kalantzis, E. Kontopoulou, M. Elkady, P. Paschou, and P. Drineas, TeraPCA: a fast and scalable software package to study genetic variation in tera-scale genotypes, Bioinformatics, btz157, https://doi.org/10.1093/bioinformatics/btz157, 2019.
A. Chowdhuri, J. Yang, and P. Drineas, Structural Conditions for Projection-Cost Preservation via Randomized Matrix Multiplication, Linear Algebra and its Applications, https://doi.org/10.1016/j.laa.2019.03.013, 2019.
P. Drineas and I. Ipsen, Low-Rank Matrix Approximations Do Not Need a Singular Value Gap, SIAM Journal on Matrix Analysis and Applications, 40(1), pp. 299-319, 2019.

2018

C. Iyer, A. Gittens, C. Carothers, and P. Drineas, Iterative Randomized Algorithms for Low Rank Approximation of Tera-scale Matrices with Small Spectral Gaps, 9th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA) 2018, held in conjunction with the International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2018.
A. Chowdhuri, J. Yang, and P. Drineas, An Iterative, Sketching-based Framework for Ridge Regression, Proceedings of the International Conference on Machine Learning (ICML), 2018.
E. Kontopoulou, A. Grama, W. Szpankowski, and P. Drineas, Randomized Linear Algebra Approaches to Estimate the Von Neumann Entropy of Density Matrices, IEEE International Symposium on Information Theory (ISIT), pp. 2486-2490, 2018.
P. Drineas, I. Ipsen, E. Kontopoulou, and M. Magdon-Ismail, Structural Convergence Results for Approximations of Dominant Subspaces from Block Krylov Spaces, SIAM Journal on Matrix Analysis and Applications, 39(2), pp. 567-586, 2018.
P. Drineas and M. W. Mahoney, Lectures on Randomized Numerical Linear Algebra, The Mathematics of Data, IAS/Park City Math. Ser., vol. 25, Amer. Math. Soc., Providence, RI, 2018.

2017

C. Boutsidis, P. Drineas, P. Kambadur, E. Kontopoulou, and A. Zouzias, A Randomized Algorithm for Approximating the Log Determinant of a Symmetric Positive Definite Matrix, Linear Algebra and its Applications, 533, pp. 95-119, 2017.
A. Kundu, P. Drineas, and M. Magdon-Ismail, Recovering PCA via Hybrid-(l1,l2) Sparse Sampling of Data Elements, Journal of Machine Learning Research, 18(75), pp. 1-34, 2017.
J. Alexander, D. Mantzaris, M. Georgitsi, P. Drineas, and P. Paschou, Variant Ranker: a web-tool to rank genomic data according to functional significance, BMC Bioinformatics, 18:341, 2017.
K. Fountoulakis, A. Kundu, E. Kontopoulou, and P. Drineas, A Randomized Rounding Algorithm for Sparse PCA, ACM Transactions on Knowledge Discovery from Data (TKDD), 11(3), pp. 1-26, 2017.
G. Stamatoyannopoulos, A. Bose, A. Teodosiadis, F. Tsetsos, A. Plantinga, N. Psatha, N. Zogas, E. Yannaki, P. Zalloua, K. K. Kidd, B. L. Browning, J. Stamatoyannopoulos, P. Paschou, and P. Drineas, Genetics of the Peleponnesean Populations and the Theory of Extinction of the Medieval Peloponnesean Greeks, European Journal of Human Genetics (EJHG), 25(5), pp. 637-645, 2017.

2016

C. Iyer, H. Avron, G. Kollias, Y. Ineichen, C. Carothers, and P. Drineas, A Randomized Least Squares Solver for Terabyte-sized Dense Overdetermined Systems, Journal of Computational Science, http://dx.doi.org/10.1016/j.jocs.2016.09.007, 2016.
C. Iyer, C. Carothers, and P. Drineas, Randomized Sketching for Large-Scale Sparse Ridge Regression Problems, Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA16), held in conjunction with the 2016 International Conference on High Performance Computing, Networking, Storage and Analysis (SC16), 2016.
N. J. Forde, A. S. Kanaan, J. Widomska, S. S. Padmanabhuni, E. Nespoli, J. Alexander, J. Rodriguez Arranz, S. Fan, R. Houssari, M. S. Nawaz, N. R. Zilhao, L. Pagliaroli, F. Rizzo, T. Aranyi, C. Barta, T. M. Boeckers, D. I. Boomsma, W. R. Buisman, J. K. Buitelaar, D. Cath, A. Dietrich, N. Driessen, P. Drineas, M. Dunlap, S. Gerasch, J. Glennon, B. Hengerer, O. A. van den Heuvel, C.e Jespersgaard, H. E. Moller, K. R. Müller-Vahl, T. Openneer, G. Poelmans, P. J. W. Pouwels, J. M. Scharf, H. Stefansson, Z. Tumer, D. Veltman, Y. D van der Werf, P. J. Hoekstra, A. Ludolph, and P. Paschou, TS-EUROTRAIN: A European-wide investigation and training network on the aetiology and pathophysiology of Gilles de la Tourette Syndrome, Frontiers in Neuroscience, 10, article 384, 2016.
F. Tsetsos, S. S. Padmanabhuni, J. Alexander, I. Karagiannidis, M. Tsifintaris, A. Topaloudi, D. Mantzaris, M. Georgitsi, P. Drineas, and P. Paschou, Meta-analysis of Tourette Syndrome and Attention Deficit Hyperactivity Disorder provides support for a shared genetic basis, Frontiers in Neuroscience, 10, article 340, 2016.
K. Clarkson, P. Drineas, M. Magdon-Ismail, M. W. Mahoney, X. Meng, and D. P. Woodruff, Faster Robust Linear Regression, SIAM Journal on Computing, 45(3), pp. 763-810, 2016.
S. Paul, M. Magdon-Ismail, and P. Drineas, Feature Selection for Linear SVMs with Provable Guarantees, Pattern Recognition, 60, pp. 205-214, 2016.
W. Mahoney and P. Drineas, RandNLA: Randomized Numerical Linear Algebra, Communications of the ACM (CACM), 59 (6), pp. 80-90, 2016.
J. Alexander, O. Kalev, S. Mehrabian, L. Traykov, M. Raycheva, D. Kanakis, P. Drineas, M. I. Lutz, T. Ströbel, T. Penz, M. Schuster, C. Bock, I. Ferrer, P. Paschou, and G. G. Kovacs, Familial early-onset dementia with complex neuropathological phenotype and genomic background, Neurobiology of Aging, 42, pp. 199-204, 2016.
S. Paul and P. Drineas, Feature Selection for Ridge Regression with Provable Guarantees, Neural Computation, MIT Press Journals, 28, pp. 716-742, 2016.
M. W. Mahoney and P. Drineas, Structural Properties Underlying high-quality Randomized Numerical Linear Algebra algorithms, CRC Handbook on Big Data, pp. 137-154, 2016.
E. Gallopoulos, P. Drineas, I. Ipsen, and M. W. Mahoney, RandNLA, Pythons, and the CUR for your Data problems, SIAM News, p. 7, February 2016.

2015

C. Iyer, H. Avron, G. Kollias, Y. Ineichen, C. Carothers, and P. Drineas, A Scalable Randomized Least Squares Solver for Dense Overdetermined Systems, Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA15), held in conjunction with the 2015 International Conference on High Performance Computing, Networking, Storage and Analysis (SC15), 2015.
A. Kundu, P. Drineas, and M. Magdon-Ismail, Approximating Sparse PCA from Incomplete Data, Proc. of Neural Information Processing Systems (NIPS), 2015.
S. Paul, M. Magdon-Ismail, and P. Drineas, Column Selection via Adaptive Sampling, Proc. of Neural Information Processing Systems (NIPS), 2015.
N. Nguyen, P. Drineas, and T. Tran, Tensor Sparsification via a Bound on the Spectral Norm of Random Tensors, Information and Inference: A Journal of the IMA, 4(3), pp. 195-229, 2015.
S. Paul, M. Magdon-Ismail, and P. Drineas, Feature Selection for Linear SVM with Provable Guarantees, Proc. of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS) and Journal of Machine Learning Research: Workshops and Conference Proceedings 38, pp. 735-743, 2015.
C. Boutsidis, A. Zouzias, M. W. Mahoney, and P. Drineas, Randomized Dimensionality Reduction for K-means Clustering, IEEE Transactions on Information Theory, 62(2), pp. 1045-1062, 2015.

2014

S. Paul and P. Drineas, Deterministic Feature Selection for Regularized Least Squares Classification, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), LNCS 8725, pp. 533-548, 2014.
P. Saurabh, C. Boutsidis, M. Magdon-Ismail, and P. Drineas, Random Projections for Support Vector Machines, ACM Transactions on Knowledge Discovery from Data, 8(4):22, 2014.
P. Paschou, P. Drineas, E. Yannaki, A. Razou, K. Kanaki, F. Tsetsos, S. Padmanabhuni, M. Michalodimitrakis, M. Renda, S. Pavlovic, A. Anagnostopoulos, J. Stamatoyannopoulos, K. K. Kidd, and G. Stamatoyannopoulos, Maritime route of colonization of Europe, Proceedings of the National Academy of Sciences, doi:10.1073/pnas.1320811111, 2014.
C. Boutsidis, P. Drineas, and M. Magdon-Ismail, Near-Optimal Column-Based Matrix Reconstruction, SIAM Journal on Computing, 43(2), pp. 687-717, 2014.
A. Kundu and P. Drineas, A Note on Randomized Element-wise Matrix Sparsification, 2014.

2013

C. Boutsidis, P. Drineas, and M. Magdon-Ismail, Near-Optimal Coresets for Least-Squares Regression, IEEE Transactions on Information Theory, 59(10), 6880 - 6892, 2013.
J. R. Hughey, P. Paschou, P. Drineas, D. Mastropaolo, D. M. Lotakis, P. A. Navas, M. Michalodimitrakis, J. A. Stamatoyannopoulos, and G. Stamatoyannopoulos, A European Population in Minoan Bronze Age Crete, Nature Communications, (4)1861 doi:10.1038/ncomms2871, 2013.
S. Paul, C. Boutsidis, M. Magdon-Ismail, and P. Drineas, Random Projections for Support Vector Machines, Proc. of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 498-506, 2013.
K. Clarkson, P. Drineas, M. Magdon-Ismail, M. W. Mahoney, X. Meng, and D. P. Woodruff, The Fast Cauchy Transform and Faster Robust Linear Regression, Proc. of the 24th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2013.

2012

P. Drineas, M. Magdon-Ismail, M. W. Mahoney, and D. Woodruff, Fast Approximation of Matrix Coherence and Statistical Leverage, Journal of Machine Learning Research, 13, pp. 3475-3506 , 2012.
V. Stathias, G. Sotiris, I. Karagiannidis, G. Bourikas, G. Martinis, D. Papazoglou, A. Tavridou, N. Papanas, E. Maltezos, M. Theodoridis, V. Vargemezis, V. Manolopoulos, W. C. Speed, J. R. Kidd, K. K. Kidd, P. Drineas, and P. Paschou, Exploring genomic structure differences and similarities between the Greek and European HapMap populations: implications for association studies, Annals of Human Genetics, 76(6), pp. 472-483, 2012.
P. Drineas, M. Magdon-Ismail, M. W. Mahoney, and D. Woodruff, Fast Approximation of Matrix Coherence and Statistical Leverage, International Conference on Machine Learning (ICML), 2012.

2011

N. Kupp, H. Huang, P. Drineas, and Y. Makris, Improving Analog and RF Device Yield through Performance Calibration, IEEE Design and Test of Computers, 28(3), pp. 64-75, 2011.
N. Kupp, H. Stratigopoulos, P. Drineas, and Y. Makris, On Proving the Efficiency of Alternative RF Tests, International Conference on Computer-Aided Design (ICCAD), pp. 762-767, 2011.
C. Boutsidis, P. Drineas, and M. Magdon-Ismail, Sparse Features for PCA-Like Linear Regression, Proc. of Neural Information Processing Systems (NIPS), 2011.
C. Boutsidis, P. Drineas, and M. Magdon-Ismail, Near-Optimal Column-Based Matrix Reconstruction, Proc. of the 52nd IEEE Symposium on Foundations of Computer Science (FOCS), 2011.
A. Javed, P. Drineas, M. W. Mahoney, P. Paschou, Efficient genome-wide selection of PCA-correlated tSNPs for genotype imputation, Annals of Human Genetics, 75(6), pp. 707-722, 2011.
J. Lewis, Z. Abas, C. Dadousis, D. Lykidis, P. Paschou, and P. Drineas, Tracing Cattle Breeds With Principal Components Analysis Ancestry Informative SNPs, PLoS ONE, 6(4): e18007, 2011.
U. Acer, P. Drineas, and A. Abouzeid, Connectivity in Time-Graphs, Pervasive and Mobile Computing, 7, pp. 160-171, 2011.
N. G. Sgourakis, M. Merced-Serrano, C. Boutsidis, P. Drineas, Z. Du, C. Wang, and A. E. Garcia, Atomic-level characterization of the ensemble of the Aβ(1-42) monomer in water using unbiased molecular dynamics simulations and spectral algorithms, Journal of Molecular Biology, 405(2), pp.570-583, 2011.
P. Drineas, M. W. Mahoney, S. Muthukrishnan, and T. Sarlos, Faster Least Squares Approximation, Numerische Mathematik, 117(2), pp. 217-249, 2011.
C. Tsourakakis, P. Drineas, E. Michelakis, I. Koutis, and C. Faloutsos, Spectral Counting of Triangles via Element-Wise Sparsification and Triangle-Based Link Recommendation, Journal of Social Network Analysis and Mining (SNAM), 1(2), pp. 75-81, 2011.
P. Drineas and A. Zouzias, A note on element-wise matrix sparsification via a matrix-valued Bernstein inequality, Information Processing Letters, 111, pp. 385-389, 2011.

2010

P. Drineas and M. W. Mahoney, Effective Resistances, Statistical Leverage, and Applications to Linear Equation Solving, arXiv1005:3097, 2010.
P. Paschou, J. Lewis, A. Javed, and P. Drineas, Ancestry Informative Markers for Fine-Scale Individual Assignment to Worldwide Populations, Journal of Medical Genetics, doi:10.1136/jmg.2010.078212, 2010.
P. Drineas, J. Lewis, and P. Paschou, Inferring Geographic Coordinates of Origin for Europeans using Small Panels of Ancestry Informative Markers, PLoS ONE, 5(8):e11892, 2010.
H-G. D. Stratigopoulos, P. Drineas, M. Slamani, and Y. Makris, RF specification test compaction using learning machines, IEEE Transactions on VLSI Systems, 18(6), pp. 1002-1006, 2010.
U. Acer, P. Drineas, and A. Abouzeid, Random walks in time-graphs, Proceedings of the Second International Workshop on Mobile Opportunistic Networking (MobiOpp), pp. 93-100, 2010.
C. Boutsidis, A. Zouzias, and P. Drineas, Random Projections for k-means Clustering, Proc. of Neural Information Processing Systems (NIPS), 2010.
N. Kupp, H. Huang, P. Drineas, and Y. Makris, Post-Production Performance Calibration in Analog/RF Devices, IEEE International Test Conference (ITC), 8.3.1-8.3.10, 2010.
C. Boutsidis, M. W. Mahoney, and P. Drineas, An improved approximation algorithm for the column subset selection problem, 2013.
P. Drineas and M. W. Mahoney, Effective Resistances, Statistical Leverage, and Applications to Linear Equation Solving, 2010.

2009

C. Boutsidis, M.W. Mahoney, and P. Drineas, Unsupervised Feature Selection for the k-means Clustering Problem, Proc. of Neural Information Processing Systems (NIPS), 2009.
C. Boutsidis and P. Drineas, Random projections for the nonnegative least-squares problem, Linear Algebra and its Applications, 431, pp. 760-771, 2009.
A. Dasgupta, P. Drineas, B. Harb, R. Kumar, and M. W. Mahoney, Sampling algorithms and coresets for lp regression, SIAM Journal on Computing, 38(5), pp. 2060-2078, 2009.
M. W. Mahoney and P. Drineas, CUR matrix decompositions for improved data analysis, Proceedings of the National Academy of Sciences, 106(3), pp. 697-702, 2009.
C. Tsourakakis, P. Drineas, E. Michelakis, I. Koutis, and C. Faloutsos, Spectral counting of triangles in power-law networks via element-wise sparsification, International Conference on Advances in Social Network Analysis and Mining (ASONAM), 2009.
C. Boutsidis, M.W. Mahoney, and P. Drineas, An improved approximation algorithm for the column subset selection problem, Proc. of the 20th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 968-977, 2009.

2008

M. W. Mahoney, M. Maggioni, and P. Drineas, Tensor-CUR decompositions for tensor-based data, SIAM Journal on Matrix Analysis and Applications, 30(2), pp. 957-987, 2008.
P. Drineas, M.W. Mahoney, and S. Muthukrishnan, Relative-error CUR matrix decompositions, SIAM Journal on Matrix Analysis and Applications, 30(2), pp. 844-881, 2008.
P. Paschou, P. Drineas, J. Lewis, C. Nievergelt, D. Nickerson, J. Smith, P. Ridker, D. Chasman, R. Krauss, and E. Ziv, Tracing sub-structure in the European American population with PCA-informative markers, PLoS Genetics, 4(7), pp. 1-13, 2008.
C. Boutsidis, M.W. Mahoney, and P. Drineas, Unsupervised feature selection for Principal Components Analysis, Proc. of the 14th Annual ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 61-69, 2008.
N. Kupp, P. Drineas, M. Slamani, and Y. Makris, Confidence estimation in non-RF to RF correlation-based specification test compaction, Proc. of the 13th European Test Symposium (ETS), pp. 35-40, 2008.
A. Dasgupta, P. Drineas, B. Harb, R. Kumar, and M. W. Mahoney, Sampling algorithms and coresets for lp regression, Proc. of the 19th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 932-941, 2008

2007

P. Paschou, E. Ziv, E. Burchard, S. Choudhry, W. Rodriguez-Cintron, M. W. Mahoney, and P. Drineas, PCA-correlated SNPs for structure identification in worldwide human populations, PLOS Genetics, 3(9), pp. 1672-1686, 2007.
P. Paschou, M. W. Mahoney, A. Javed, J. Kidd, A. Pakstis, S. Gu, K. Kidd, and P. Drineas, Intra- and inter-population genotype reconstruction from tagging SNPs, Genome Research, 17(1), pp. 96-107, 2007.
P. Drineas and M. W. Mahoney, A randomized algorithm for a tensor-based generalization of the SVD, Linear Algebra and its Applications, 420, pp. 553-571, 2007.
P. Drineas, M. W. Mahoney, and R. Kannan, Sampling sub-problems of heterogeneous max-cut problems and approximation algorithms, Random Structures and Algorithms,32(3), pp. 307-333, 2007.
A. Dasgupta, P. Drineas, B. Harb, V. Josifovski, and M. Mahoney, Feature selection methods for text classification, Proc. of the 13th Annual ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 230-239, 2007.
H-G. D. Stratigopoulos, P. Drineas, M. Slamani, and Y. Makris, Non-RF to RF test correlation using learning machines: a case study, Proc. of the 25th IEEE VLSITest Symposium (VTS), pp. 9-14, 2007.

2006

G.H. Golub, M.W. Mahoney, P. Drineas, and L.-H. Lim, MMDS 2006: bridging the gap between numerical linear algebra, theoretical computer science, and data applications, SIAM News, Oct 2006.
P. Drineas, R. Kannan, and M. W. Mahoney, Fast monte carlo algorithms for matrices I: approximating matrix multiplication, SIAM Journal on Computing, 36(1), pp. 132-157, 2006.
P. Drineas, R. Kannan, and M. W. Mahoney, Fast monte carlo algorithms for matrices II: computing a low rank approximation to a matrix, SIAM Journal on Computing, 36(1), pp. 158-183, 2006.
P. Drineas, R. Kannan, and M. W. Mahoney, Fast monte carlo algorithms for matrices III: computing a compressed approximate matrix decomposition, SIAM Journal on Computing, 36(1), pp. 184-206, 2006.
S. Almukhaizim, P. Drineas, and Y. Makris, Entropy-driven parity tree selection for low-overhead concurrent error detection in finite state machines, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 25(8), pp. 1547-1554, 2006.
P. Drineas, M. W. Mahoney, and S. Muthukrishnan, Subspace sampling and relative error matrix approximation: column-based methods, Proc. of APPROX-RANDOM, pp. 316-326, 2006.
P. Drineas, M. W. Mahoney, and S. Muthukrishnan, Subspace sampling and relative error matrix approximation: column-row-based methods, Proc. of the 14th Annual European Symposium on Algorithms (ESA), pp. 304-314, 2006.
P. Drineas, M. W. Mahoney, and S. Muthukrishnan, Polynomial time algorithm for column-row based relative error low-rank matrix approximation, DIMACS Technical Report 2006-04, 2006.
P. Drineas, A. Javed, M. Magdon-Ismail, G. Pandurangan, R. Virrankoski, and A. Savvides, Distance matrix reconstruction from incomplete distance information for sensor network localization, Proc. of the 3rd Annual IEEE Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), pp. 536-544, 2006.
P. Drineas and M. W. Mahoney, Randomized algorithms for matrices and massive data sets, Proc. of the 32nd Annual Conference on Very Large Data Bases (VLDB), p. 1269, 2006.
M. W. Mahoney, M. Maggioni, and P. Drineas, Tensor-CUR decompositions for tensor-based data, Proc. of the 12th Annual ACM Conference on Knowledge Discovery andData Mining (KDD), pp. 327-336, 2006.
P. Drineas, M. W. Mahoney, and S. Muthukrishnan, Sampling algorithms for l2 regression and applications, Proc. of the 17th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 1127-1136, 2006.

2005

P. Drineas and M. W. Mahoney, On the Nystrom method for approximating a Gram matrix for improved kernel-based learning, Journal of Machine Learning Research 6, pp. 2153-2175, 2005.
S. Almukhaizim, P. Drineas, and Y. Makris, Compaction-based concurrent error detection for digital circuits, Microelectronics Journal, 36(9), pp. 856-862, Elsevier, 2005.
D. Freedman and P. Drineas, Energy minimization via graph cuts: settling what is possible, Proc. of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 939-946, 2005.
P. Drineas and M. W. Mahoney, Approximating a Gram matrix for improved kernel-based learning, Proc. of the 18th Annual Symposium on Computational Learning Theory (COLT), pp. 323-337, 2005.
P. Drineas, R. Kannan, and M. W. Mahoney, Sampling sub-problems of heterogeneous max-cut problems and approximation algorithms, Proc. of the 22nd Annual Symposium on Theoretical Aspects of Computer Science (STACS), Lecture Notes in ComputerScience 3404, pp. 57-68, 2005.

2004

P. Drineas, R. Kannan, A. Frieze, S. Vempala, and V. Vinay, Clustering of large graphs via the singular value decomposition, Machine Learning (56), pp. 9-33, 2004.
K. Akcoglu, P. Drineas, and M. Kao, Fast universalization of investment strategies, SIAM Journal on Computing 34(1), pp. 1-22, 2004.
P. Drineas, M. Krishnamoorthy, D. Sofka, and B. Yener, Studying E-mail graphs for intelligence monitoring and analysis in the absence of semantic information, Proc. of the Symposium on Intelligence and Security Informatics, Lecture Notes in Computer Science 3073, pp. 297-306, 2004.
P. Drineas, Pass efficient algorithms for approximating large matrices, Mathematisches Forschungsinstitut Oberwolfach (MFO) Workshop on Approximation Algorithms for NP-Hard Problems, Oberwolfach, 2004.
S. Almukhaizim, P. Drineas, and Y. Makris, Cost-driven selection of parity trees, Proc. of the IEEE VLSI Test Symposium (VTS), pp. 319-324, 2004.
S. Almukhaizim, P. Drineas, and Y. Makris, Concurrent error detection for combinational and sequential logic via output compaction, Proc. of the IEEE International Symposium on Quality Electronic Design (ISQED), pp. 459-464, 2004.
S. Almukhaizim, P. Drineas, and Y. Makris, On concurrent error detection with bounded latency in FSMs, Proc. of the IEEE Design Automation and Test in Europe Conference(DATE), pp. 596-601, 2004.

2003

P. Drineas and Y. Makris, SPaRe: selective partial replication for concurrent fault detection in FSMs, IEEE Transactions on Instrumentation and Measurement, 52(6), pp. 1729-1737, 2003.
P. Drineas, E. Drinea, and P. Huggins, An experimental evaluation of a monte carlo algorithm for singular value decomposition, Y. Manolopoulos et. al. (Eds.): Revised Selected Papers from the 8th Panhellenic Conference on Informatics, Lecture Notes in Computer Science 2563, pp. 279-296, 2003.
P. Drineas and R. Kannan, Pass efficient algorithms for approximating large matrices, Proc. of the 14th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 223-232, 2003.
S. Almukhaizim, P. Drineas, and Y. Makris, On Compaction-based concurrent error detection, Proc. of the IEEE On-Line Test Symposium, pp. 157, 2003.
P. Drineas and Y. Makris, Independent test sequence compaction through integer programming, Proc. of the IEEE International Conference on Computer Design (ICCD), pp. 380-386, 2003.
P. Drineas and Y. Makris, Non-intrusive concurrent error detection in FSMs through State/Output compaction and monitoring via parity trees, Proc. of the Design Automation and Test in Europe Conference (DATE), pp. 1164-1165, 2003.
P. Drineas and Y. Makris, SPaRe: selective partial replication for concurrent fault detection in FSMs, Proc. of the IEEE International Conference on VLSI Design, pp. 84-91, 2003.
P. Drineas and Y. Makris, On the Compaction of Independent Test Sequences for Sequential Circuits, IEEE European Test Workshop (ETW), 2003.
P. Drineas and Y. Makris, Concurrent fault detection in random combinational logic, Proc. of the IEEE International Symposium on Quality Electronic Design (ISQED), pp. 425-430, 2003.

2002 and older

P. Drineas, I. Kerenidis, and P. Raghavan, Competitive recommendation systems, Proc. of the 34th ACM Symposium on Theory of Computing (STOC), pp. 82-90, 2002.
K. Akcoglu, P. Drineas, and M. Kao, Fast universalization of investment strategies with provably good relative returns, Proc. of the 29th International Colloquium on Automata, Languages and Programming (ICALP), pp. 888-900, 2002.
P. Drineas and Y. Makris, Non-intrusive design of concurrently self-testable FSMs, Proc. of the IEEE Asian Test Symposium (ATS), pp. 33-38, 2002.
E. Drinea, P. Drineas, and P. Huggins, A randomized singular value decomposition algorithm for image processing applications, Proc. of the 8th Panhellenic Conference on Informatics, pp. 278-288, 2001.
P. Drineas and R. Kannan, Fast monte carlo algorithms for approximate matrix multiplication, Proc. of the 42nd IEEE Symposium on Foundations of Computer Science (FOCS), pp. 452-459, 2001.
P. Drineas, R. Kannan, A. Frieze, S. Vempala, and V. Vinay, Clustering in large graphs and matrices, Proc. of the 10th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 291-299, 1999.