Maxim Raginsky

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Rebecca WillettUniversity of Chicago, Professor of Statistics and Computer ScienceVerified email at uchicago.edu
Svetlana LazebnikUniversity of Illinois at Urbana-ChampaignVerified email at illinois.edu
Alexander RakhlinProfessor, MITVerified email at mit.edu
Igal SasonProfessor, Technion, Haifa, IsraelVerified email at ee.technion.ac.il
Roummel MarciaProfessor of Applied Mathematics, University of California, MercedVerified email at ucmerced.edu
Angelia NedichProfessor, Arizona State UniversityVerified email at asu.edu
Tamer BaşarSwanlund Endowed Chair Emeritus & CAS Professor Emeritus of ECE, University of IllinoisVerified email at illinois.edu
Naci SaldiAssistant Professor, Bilkent UniversityVerified email at bilkent.edu.tr
Soomin LeeAmazon Alexa Shopping ResearchVerified email at amazon.com
Jorge SilvaSAS InstituteVerified email at sas.com
Ehsan ShafieepoorfardUniversity of Illinois at Urbana-ChampaignVerified email at illinois.edu
Kalyani KrishnamurthySenior Algorithm Developer, Pendar TechnologiesVerified email at pendar.tech
R. CalderbankBell Labs, AT&T Labs, Princeton University, Duke UniversityVerified email at duke.edu
Sina JafarpourResearch Scientist, FacebookVerified email at fb.com
Sean MeynProfessor of ECE and Robert C. Pittman Eminent Scholar ChairVerified email at ece.ufl.edu
Todd ColemanAssociate Professor of Bioengineering, Stanford UniversityVerified email at stanford.edu
Serdar YükselQueen's UniversityVerified email at mast.queensu.ca
Andrew SingerProfessor of Electrical and Computer Engineering, University of Illinois at Urbana ChampaignVerified email at uiuc.edu
Sergio VerduProfessor of Electrical Engineering, Princeton UniversityVerified email at princeton.edu
Mehmet Ali DönmezUniversity of Illinois at Urbana ChampaignVerified email at illinois.edu

Maxim Raginsky

Professor of Electrical and Computer Engineering, University of Illinois, Urbana-Champaign

Verified email at illinois.edu - Homepage

Machine Learning Control Theory Optimization Applied Probability Information Theory


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Locality-sensitive binary codes from shift-invariant kernels M Raginsky, S Lazebnik Advances in neural information processing systems 22, 2009	774	2009
Non-convex learning via stochastic gradient langevin dynamics: a nonasymptotic analysis M Raginsky, A Rakhlin, M Telgarsky Conference on Learning Theory, 1674-1703, 2017	421	2017
Information-theoretic analysis of generalization capability of learning algorithms A Xu, M Raginsky Advances in Neural Information Processing Systems 30, 2017	307	2017
Supervised learning of quantizer codebooks by information loss minimization S Lazebnik, M Raginsky IEEE transactions on pattern analysis and machine intelligence 31 (7), 1294-1309, 2008	272	2008
Concentration of measure inequalities in information theory, communications, and coding M Raginsky, I Sason Foundations and Trends® in Communications and Information Theory 10 (1-2), 1-246, 2013	228	2013
Compressed sensing performance bounds under Poisson noise M Raginsky, RM Willett, ZT Harmany, RF Marcia IEEE Transactions on Signal Processing 58 (8), 3990-4002, 2010	174	2010
Minimax statistical learning with wasserstein distances J Lee, M Raginsky Advances in Neural Information Processing Systems 31, 2018	145*	2018
Neural stochastic differential equations: Deep latent gaussian models in the diffusion limit B Tzen, M Raginsky arXiv preprint arXiv:1905.09883, 2019	122	2019
Strong Data Processing Inequalities and-Sobolev Inequalities for Discrete Channels M Raginsky IEEE Transactions on Information Theory 62 (6), 3355-3389, 2016	107	2016
Sequential anomaly detection in the presence of noise and limited feedback M Raginsky, RM Willett, C Horn, J Silva, RF Marcia IEEE Transactions on Information Theory 58 (8), 5544-5562, 2012	103	2012
Information-based complexity, feedback and dynamics in convex programming M Raginsky, A Rakhlin IEEE Transactions on Information Theory 57 (10), 7036-7056, 2011	84	2011
A fidelity measure for quantum channels M Raginsky Physics Letters A 290 (1-2), 11-18, 2001	83	2001
Markov--Nash equilibria in mean-field games with discounted cost N Saldi, T Basar, M Raginsky SIAM Journal on Control and Optimization 56 (6), 4256-4287, 2018	82	2018
Information-theoretic analysis of stability and bias of learning algorithms M Raginsky, A Rakhlin, M Tsao, Y Wu, A Xu 2016 IEEE Information Theory Workshop (ITW), 26-30, 2016	66	2016
Continuous-time stochastic mirror descent on a network: Variance reduction, consensus, convergence M Raginsky, J Bouvrie 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 6793-6800, 2012	66	2012
Stochastic dual averaging for decentralized online optimization on time-varying communication graphs S Lee, A Nedić, M Raginsky IEEE Transactions on Automatic Control 62 (12), 6407-6414, 2017	60	2017
Operational distance and fidelity for quantum channels VP Belavkin, GM D’Ariano, M Raginsky Journal of mathematical physics 46 (6), 062106, 2005	58	2005
Online Markov decision processes with Kullback–Leibler control cost P Guan, M Raginsky, RM Willett IEEE Transactions on Automatic Control 59 (6), 1423-1438, 2014	57	2014
Estimation of intrinsic dimensionality using high-rate vector quantization M Raginsky, S Lazebnik Advances in neural information processing systems 18, 2005	57	2005
Performance bounds for expander-based compressed sensing in Poisson noise M Raginsky, S Jafarpour, ZT Harmany, RF Marcia, RM Willett, ... IEEE Transactions on Signal Processing 59 (9), 4139-4153, 2011	56	2011

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