Anastasios Kyrillidis

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Volkan CevherAssociate Professor, LIONS, EPFL. Amazon Scholar.Verified email at epfl.ch
Sujay SanghaviAssociate Professor, Electrical and Computer Engineering, University of Texas, AustinVerified email at mail.utexas.edu
Constantine CaramanisProfessor of Electrical and Computer Engineering, UT AustinVerified email at utexas.edu
Cameron WolfePhD Student, Rice UniversityVerified email at rice.edu
Chen DunPh.D student, Rice UniversityVerified email at rice.edu
Dohyung ParkThe University of Texas at AustinVerified email at utexas.edu
Quoc Tran-DinhDepartment of Statistics and Operations Research, UNCVerified email at email.unc.edu
Srinadh BhojanapalliResearch Scientist, Google ResearchVerified email at google.com
John ChenRice UniversityVerified email at rice.edu
Amir KalevInformation Sciences Institute, University of Southern CaliforniaVerified email at isi.edu
Chris JermaineAssociate Professor of Computer Science, Rice UniversityVerified email at cs.rice.edu
Stephen BeckerAssociate Professor at University of ColoradoVerified email at colorado.edu
Megasthenis AsterisUniversity of Texas at AustinVerified email at utexas.edu
Fangshuo LiaoPh.D. Student, Rice UniversityVerified email at rice.edu
Dimitris PapailiopoulosAssociate Professor, University of Wisconsin-MadisonVerified email at papail.io
Vatsal ShahAmazonVerified email at utexas.edu
Junhyung Lyle KimPh.D. Student, Computer Science Department, Rice UniversityVerified email at rice.edu
Christoph KochProfessor of Computer Science, EPFLVerified email at epfl.ch
Richard BaraniukC. Sidney Burrus Professor, Electrical & Computer Engineering, Rice UniversityVerified email at rice.edu
Rajiv KhannaAssistant Prof, PurdueCSVerified email at purdue.edu

Anastasios Kyrillidis

Assistant Professor, Computer Science Department, Rice University

Verified email at rice.edu - Homepage

Optimization Nonconvex optimization Large scale optimization


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Dropping convexity for faster semi-definite optimization S Bhojanapalli, A Kyrillidis, S Sanghavi Conference on Learning Theory, 530-582, 2016	182	2016
Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach D Park, A Kyrillidis, C Caramanis, S Sanghavi Artificial Intelligence and Statistics, pp. 65-74, 2016	172	2016
Multi-way compressed sensing for sparse low-rank tensors ND Sidiropoulos, A Kyrillidis IEEE Signal Processing Letters, 1-1, 2012	166	2012
Finding low-rank solutions via non-convex matrix factorization, efficiently and provably D Park, A Kyrillidis, C Caramanis, S Sanghavi SIAM Journal on Imaging Sciences 11 (4), 2165–2204, 2018	152*	2018
Sparse projections onto the simplex A Kyrillidis, S Becker, V Cevher, C Koch Proceedings of The 30th International Conference on Machine Learning 28, 235–243, 2012	107*	2012
Composite self-concordant minimization. Q Tran-Dinh, A Kyrillidis, V Cevher J. Mach. Learn. Res. 16 (1), 371-416, 2015	99	2015
Current progress and open challenges for applying deep learning across the biosciences N Sapoval, A Aghazadeh, MG Nute, DA Antunes, A Balaji, R Baraniuk, ... Nature Communications 13 (1), 1728, 2022	96	2022
Provable deterministic leverage score sampling D Papailiopoulos, A Kyrillidis, C Boutsidis Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014	93	2014
Matrix recipes for hard thresholding methods A Kyrillidis, V Cevher Journal of Mathematical Imaging and Vision, 2012	86	2012
Provable compressed sensing quantum state tomography via non-convex methods A Kyrillidis, A Kalev, D Park, S Bhojanapalli, C Caramanis, S Sanghavi npj Quantum Information 4 (1), 36, 2018	71*	2018
Recipes on hard thresholding methods A Kyrillidis, V Cevher 2011 4th IEEE International Workshop on Computational Advances in Multi …, 2011	55	2011
Combinatorial selection and least absolute shrinkage via the CLASH algorithm A Kyrillidis, V Cevher 2012 IEEE International Symposium on Information Theory Proceedings, 2216-2220, 2012	48*	2012
Statistical inference using SGD T Li, L Liu, A Kyrillidis, C Caramanis Thirty-Second AAAI Conference on Artificial Intelligence, 3571-3578, 2017	39	2017
Mlsys: The new frontier of machine learning systems A Ratner, D Alistarh, G Alonso, DG Andersen, P Bailis, S Bird, N Carlini, ... arXiv preprint arXiv:1904.03257, 2019	38	2019
Group-sparse model selection: Hardness and relaxations L Baldassarre, N Bhan, V Cevher, A Kyrillidis, S Satpathi IEEE Transactions on Information Theory 62 (11), 6508-6534, 2016	38	2016
IHT dies hard: Provable accelerated iterative hard thresholding R Khanna, A Kyrillidis Proceedings of the Twenty-First International Conference on Artificial …, 2017	37	2017
Sparse PCA via Bipartite Matchings M Asteris, D Papailiopoulos, A Kyrillidis, A Dimakis Neural Information Processing Systems 2015 28, 1-9, 2015	36	2015
Distributed learning of fully connected neural networks using independent subnet training B Yuan, CR Wolfe, C Dun, Y Tang, A Kyrillidis, C Jermaine Proceedings of the VLDB Endowment 15 (8), 1581-1590, 2022	35*	2022
Pipegcn: Efficient full-graph training of graph convolutional networks with pipelined feature communication C Wan, Y Li, CR Wolfe, A Kyrillidis, NS Kim, Y Lin arXiv preprint arXiv:2203.10428, 2022	35	2022
A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions QT Dinh, A Kyrillidis, V Cevher Proceedings of The 30th International Conference on Machine Learning 28, 271–279, 2013	35	2013

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