Maxim Raginsky
Maxim Raginsky
Professor of Electrical and Computer Engineering, University of Illinois, Urbana-Champaign
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Locality-sensitive binary codes from shift-invariant kernels
M Raginsky, S Lazebnik
Advances in neural information processing systems 22, 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
Information-theoretic analysis of generalization capability of learning algorithms
A Xu, M Raginsky
Advances in neural information processing systems 30, 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
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
Minimax statistical learning with wasserstein distances
J Lee, M Raginsky
Advances in Neural Information Processing Systems 31, 2018
Neural stochastic differential equations: Deep latent gaussian models in the diffusion limit
B Tzen, M Raginsky
arXiv preprint arXiv:1905.09883, 2019
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
Strong Data Processing Inequalities and-Sobolev Inequalities for Discrete Channels
M Raginsky
IEEE Transactions on Information Theory 62 (6), 3355-3389, 2016
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
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
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
Theoretical guarantees for sampling and inference in generative models with latent diffusions
B Tzen, M Raginsky
Conference on Learning Theory, 3084-3114, 2019
Information-based complexity, feedback and dynamics in convex programming
M Raginsky, A Rakhlin
IEEE Transactions on Information Theory 57 (10), 7036-7056, 2011
A fidelity measure for quantum channels
M Raginsky
Physics Letters A 290 (1-2), 11-18, 2001
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
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
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
Operational distance and fidelity for quantum channels
VP Belavkin, GM D’Ariano, M Raginsky
Journal of mathematical physics 46 (6), 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
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