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
Cameron WolfePhD Student, Rice UniversityVerified email at rice.edu
Chen DunPh.D, Rice UniversityVerified email at rice.edu
Constantine CaramanisProfessor of Electrical and Computer Engineering, UT AustinVerified email at utexas.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
Fangshuo LiaoPh.D. Student, Rice UniversityVerified email at rice.edu
John ChenRice UniversityVerified email at rice.edu
Srinadh BhojanapalliResearch Scientist, Google ResearchVerified email at google.com
Junhyung Lyle KimPh.D. Student, Computer Science Department, Rice UniversityVerified email at rice.edu
Chris JermaineAssociate Professor of Computer Science, Rice UniversityVerified email at cs.rice.edu
Amir KalevInformation Sciences Institute, University of Southern CaliforniaVerified email at isi.edu
Stephen BeckerAssociate Professor at University of ColoradoVerified email at colorado.edu
Richard BaraniukC. Sidney Burrus Professor, Electrical & Computer Engineering, Rice UniversityVerified email at rice.edu
Megasthenis AsterisUniversity of Texas at AustinVerified email at utexas.edu
Vatsal ShahDoordashVerified email at utexas.edu
Dimitris PapailiopoulosResearcher at Microsoft Research & Assoc. Professor at University of WisconsinVerified email at papail.io
Yuxin TangRice UniversityVerified email at rice.edu
Anshumali ShrivastavaRice University, ThirdAI Corp.Verified email at rice.edu

Anastasios Kyrillidis

Associate Professor, Computer Science Department, Ken Kennedy Institute Fellow, Rice University

Verified email at rice.edu - Homepage

Optimization Nonconvex optimization Large scale optimization


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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	213	2022
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	188	2016
Dropping convexity for faster semi-definite optimization S Bhojanapalli, A Kyrillidis, S Sanghavi Conference on Learning Theory, 530-582, 2016	184	2016
Multi-way compressed sensing for sparse low-rank tensors ND Sidiropoulos, A Kyrillidis IEEE Signal Processing Letters, 1-1, 2012	165	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	160*	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	111	2012
Composite self-concordant minimization. Q Tran-Dinh, A Kyrillidis, V Cevher J. Mach. Learn. Res. 16 (1), 371-416, 2015	105	2015
Provable deterministic leverage score sampling D Papailiopoulos, A Kyrillidis, C Boutsidis Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014	101	2014
Matrix recipes for hard thresholding methods A Kyrillidis, V Cevher Journal of Mathematical Imaging and Vision, 2012	86	2012
Scissorhands: Exploiting the persistence of importance hypothesis for llm kv cache compression at test time Z Liu, A Desai, F Liao, W Wang, V Xie, Z Xu, A Kyrillidis, A Shrivastava Advances in Neural Information Processing Systems 36, 2024	81	2024
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	76*	2018
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	66	2022
Statistical inference using SGD T Li, L Liu, A Kyrillidis, C Caramanis Thirty-Second AAAI Conference on Artificial Intelligence, 3571-3578, 2017	55	2017
Recipes on hard thresholding methods A Kyrillidis, V Cevher 2011 4th IEEE International Workshop on Computational Advances in Multi …, 2011	54	2011
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, 2022	53*	2022
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	50*	2012
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	48	2019
Demon: Momentum Decay for Improved Neural Network Training J Chen, C Wolfe, Z Li, A Kyrillidis ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and …, 2020	42*	2020
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	39	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

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