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ORCIDMaxim Raginsky
Person information
- affiliation: University of Illinois at Urbana-Champaign, Coordinated Science Laboratory, Urbana, IL, USA
- affiliation: Duke University, Department of Electrical and Computer Engineering, Durham, NC, USA
- affiliation (PhD 202): Northwestern University, Department of Electrical and Computer Engineering, Evanston, IL, USA
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2020 – today
- 2024
[i64]Shizhuo Dylan Zhang, Curt Tigges, Zory Zhang, Stella Biderman, Maxim Raginsky, Talia Ringer:
Transformer-Based Models Are Not Yet Perfect At Learning to Emulate Structural Recursion. CoRR abs/2401.12947 (2024)- [i63]Joshua Hanson, Maxim Raginsky:
Rademacher Complexity of Neural ODEs via Chen-Fliess Series. CoRR abs/2401.16655 (2024) - [i62]Tanya Veeravalli, Maxim Raginsky:
Revisiting Stochastic Realization Theory using Functional Itô Calculus. CoRR abs/2402.10157 (2024) - 2023
- [j34]Belinda Tzen, Anant Raj
, Maxim Raginsky, Francis R. Bach:
Variational Principles for Mirror Descent and Mirror Langevin Dynamics. IEEE Control. Syst. Lett. 7: 1542-1547 (2023) - [j33]Naci Saldi, Tamer Basar, Maxim Raginsky:
Partially Observed Discrete-Time Risk-Sensitive Mean Field Games. Dyn. Games Appl. 13(3): 929-960 (2023) - [c74]Tanya Veeravalli, Maxim Raginsky:
A Constructive Approach to Function Realization by Neural Stochastic Differential Equations. CDC 2023: 6364-6369 - [c73]Yifeng Chu, Maxim Raginsky:
Majorizing Measures, Codes, and Information. ISIT 2023: 660-665 - [c72]Tanya Veeravalli, Maxim Raginsky:
Nonlinear Controllability and Function Representation by Neural Stochastic Differential Equations. L4DC 2023: 838-850 - [c71]Yifeng Chu, Maxim Raginsky:
A unified framework for information-theoretic generalization bounds. NeurIPS 2023 - [i61]Belinda Tzen, Anant Raj, Maxim Raginsky, Francis R. Bach:
Variational Principles for Mirror Descent and Mirror Langevin Dynamics. CoRR abs/2303.09532 (2023) - [i60]Yifeng Chu, Maxim Raginsky:
A Chain Rule for the Expected Suprema of Bernoulli Processes. CoRR abs/2304.14474 (2023) - [i59]Yifeng Chu, Maxim Raginsky:
Majorizing Measures, Codes, and Information. CoRR abs/2305.02960 (2023) - [i58]Yifeng Chu, Maxim Raginsky:
A unified framework for information-theoretic generalization bounds. CoRR abs/2305.11042 (2023) - [i57]Shizhuo Dylan Zhang, Curt Tigges, Stella Biderman, Maxim Raginsky, Talia Ringer:
Can Transformers Learn to Solve Problems Recursively? CoRR abs/2305.14699 (2023) - [i56]Tanya Veeravalli, Maxim Raginsky:
A Constructive Approach to Function Realization by Neural Stochastic Differential Equations. CoRR abs/2307.00215 (2023) - [i55]Fredrik Hellström, Giuseppe Durisi, Benjamin Guedj, Maxim Raginsky:
Generalization Bounds: Perspectives from Information Theory and PAC-Bayes. CoRR abs/2309.04381 (2023) - 2022
- [j32]Ali Devran Kara, Maxim Raginsky, Serdar Yüksel:
Robustness to incorrect models and data-driven learning in average-cost optimal stochastic control. Autom. 139: 110179 (2022) - [j31]Aolin Xu, Maxim Raginsky:
Minimum Excess Risk in Bayesian Learning. IEEE Trans. Inf. Theory 68(12): 7935-7955 (2022) - [c70]Joshua Hanson, Maxim Raginsky:
Fitting an immersed submanifold to data via Sussmann's orbit theorem. CDC 2022: 5323-5328 - [c69]Alan Yang, Jie Xiong, Maxim Raginsky, Elyse Rosenbaum:
Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits. L4DC 2022: 663-675 - [e1]Po-Ling Loh, Maxim Raginsky:
Conference on Learning Theory, 2-5 July 2022, London, UK. Proceedings of Machine Learning Research 178, PMLR 2022 [contents] - [i54]Alan Yang, Jie Xiong, Maxim Raginsky, Elyse Rosenbaum:
Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits. CoRR abs/2202.06453 (2022) - [i53]Joshua Hanson, Maxim Raginsky:
Fitting an immersed submanifold to data via Sussmann's orbit theorem. CoRR abs/2204.01119 (2022) - [i52]Tanya Veeravalli, Maxim Raginsky:
Nonlinear controllability and function representation by neural stochastic differential equations. CoRR abs/2212.00896 (2022) - 2021
- [c68]Mehmet A. Donmez, Jeffrey Ludwig, Maxim Raginsky, Andrew C. Singer:
EE-Grad: Exploration and Exploitation for Cost-Efficient Mini-Batch SGD. ACSCC 2021: 490-497 - [c67]Todd P. Coleman, Maxim Raginsky:
Sampling, variational Bayesian inference, and conditioned stochastic differential equations. CDC 2021: 3054-3059 - [c66]Joshua Hanson, Maxim Raginsky, Eduardo D. Sontag:
Learning Recurrent Neural Net Models of Nonlinear Systems. L4DC 2021: 425-435 - [c65]Jie Xiong, Alan Yang, Maxim Raginsky, Elyse Rosenbaum:
Neural Networks for Transient Modeling of Circuits : Invited Paper. MLCAD 2021: 1-7 - [c64]Hrayr Harutyunyan, Maxim Raginsky, Greg Ver Steeg, Aram Galstyan:
Information-theoretic generalization bounds for black-box learning algorithms. NeurIPS 2021: 24670-24682 - [i51]Hrayr Harutyunyan, Maxim Raginsky, Greg Ver Steeg, Aram Galstyan:
Information-theoretic generalization bounds for black-box learning algorithms. CoRR abs/2110.01584 (2021) - 2020
- [j30]Naci Saldi, Tamer Basar, Maxim Raginsky:
Approximate Markov-Nash Equilibria for Discrete-Time Risk-Sensitive Mean-Field Games. Math. Oper. Res. 45(4): 1596-1620 (2020) - [j29]Naveen Goela, Maxim Raginsky:
Channel Polarization Through the Lens of Blackwell Measures. IEEE Trans. Inf. Theory 66(10): 6222-6241 (2020) - [c63]Joshua Hanson, Maxim Raginsky:
Universal Simulation of Stable Dynamical Systems by Recurrent Neural Nets. L4DC 2020: 384-392 - [c62]Alan Yang, AmirEmad Ghassami, Maxim Raginsky, Negar Kiyavash, Elyse Rosenbaum:
Model-Augmented Conditional Mutual Information Estimation for Feature Selection. UAI 2020: 1139-1148 - [i50]Belinda Tzen, Maxim Raginsky:
A mean-field theory of lazy training in two-layer neural nets: entropic regularization and controlled McKean-Vlasov dynamics. CoRR abs/2002.01987 (2020) - [i49]Naci Saldi, Tamer Basar, Maxim Raginsky:
Partially Observed Discrete-Time Risk-Sensitive Mean Field Games. CoRR abs/2003.11987 (2020) - [i48]Joshua Hanson, Maxim Raginsky, Eduardo D. Sontag:
Learning Recurrent Neural Net Models of Nonlinear Systems. CoRR abs/2011.09573 (2020) - [i47]Aolin Xu, Maxim Raginsky:
Minimum Excess Risk in Bayesian Learning. CoRR abs/2012.14868 (2020)
2010 – 2019
- 2019
- [j28]Naci Saldi, Tamer Basar, Maxim Raginsky:
Approximate Nash Equilibria in Partially Observed Stochastic Games with Mean-Field Interactions. Math. Oper. Res. 44(3): 1006-1033 (2019) - [j27]Jaeho Lee, Maxim Raginsky:
Learning Finite-Dimensional Coding Schemes with Nonlinear Reconstruction Maps. SIAM J. Math. Data Sci. 1(3): 617-642 (2019) - [c61]Naci Saldi, Tamer Basar, Maxim Raginsky:
Partially-Observed Discrete-Time Risk-Sensitive Mean-Field Games. CDC 2019: 317-322 - [c60]Noyan C. Sevüktekin, Maxim Raginsky, Andrew C. Singer:
Linear Noisy Networks with Stochastic Components. CDC 2019: 5386-5391 - [c59]Ali Devran Kara, Maxim Raginsky, Serdar Yüksel:
Robustness to Incorrect Models in Average-Cost Optimal Stochastic Control. CDC 2019: 7970-7975 - [c58]Belinda Tzen, Maxim Raginsky:
Theoretical guarantees for sampling and inference in generative models with latent diffusions. COLT 2019: 3084-3114 - [c57]Joshua Hanson, Maxim Raginsky:
Universal Approximation of Input-Output Maps by Temporal Convolutional Nets. NeurIPS 2019: 14048-14058 - [i46]Belinda Tzen, Maxim Raginsky:
Theoretical guarantees for sampling and inference in generative models with latent diffusions. CoRR abs/1903.01608 (2019) - [i45]Belinda Tzen, Maxim Raginsky:
Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit. CoRR abs/1905.09883 (2019) - [i44]Joshua Hanson, Maxim Raginsky:
Universal Approximation of Input-Output Maps by Temporal Convolutional Nets. CoRR abs/1906.09211 (2019) - [i43]Alan Yang, AmirEmad Ghassami, Maxim Raginsky, Negar Kiyavash, Elyse Rosenbaum:
Model-Augmented Nearest-Neighbor Estimation of Conditional Mutual Information for Feature Selection. CoRR abs/1911.04628 (2019) - 2018
- [j26]Naci Saldi, Tamer Basar, Maxim Raginsky:
Markov-Nash Equilibria in Mean-Field Games with Discounted Cost. SIAM J. Control. Optim. 56(6): 4256-4287 (2018) - [j25]Soomin Lee, Angelia Nedic, Maxim Raginsky:
Coordinate Dual Averaging for Decentralized Online Optimization With Nonseparable Global Objectives. IEEE Trans. Control. Netw. Syst. 5(1): 34-44 (2018) - [j24]Ehsan Shafieepoorfard, Maxim Raginsky:
Sequential Empirical Coordination Under an Output Entropy Constraint. IEEE Trans. Inf. Theory 64(10): 6830-6841 (2018) - [c56]Yanina Shkel, Maxim Raginsky, Sergio Verdú:
Sequential prediction with coded side information under logarithmic loss. ALT 2018: 753-769 - [c55]Belinda Tzen, Tengyuan Liang, Maxim Raginsky:
Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability. COLT 2018: 857-875 - [c54]Yang Xiu, Samuel Sagan, Advika Battini, Xiao Ma, Maxim Raginsky, Elyse Rosenbaum:
Stochastic modeling of air electrostatic discharge parameters. IRPS 2018: 2 - [c53]Yanina Shkel, Maxim Raginsky, Sergio Verdú:
Universal Compression, List Decoding, and Logarithmic Loss. ISIT 2018: 206-210 - [c52]Jaeho Lee, Maxim Raginsky:
Minimax Statistical Learning with Wasserstein distances. NeurIPS 2018: 2692-2701 - [i42]Belinda Tzen, Tengyuan Liang, Maxim Raginsky:
Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability. CoRR abs/1802.06439 (2018) - [i41]Naci Saldi, Tamer Basar, Maxim Raginsky:
Discrete-time Risk-sensitive Mean-field Games. CoRR abs/1808.03929 (2018) - [i40]Naveen Goela, Maxim Raginsky:
Channel Polarization through the Lens of Blackwell Measures. CoRR abs/1809.05073 (2018) - [i39]Jaeho Lee, Maxim Raginsky:
Learning finite-dimensional coding schemes with nonlinear reconstruction maps. CoRR abs/1812.09658 (2018) - 2017
- [j23]Soomin Lee, Angelia Nedic, Maxim Raginsky:
Stochastic Dual Averaging for Decentralized Online Optimization on Time-Varying Communication Graphs. IEEE Trans. Autom. Control. 62(12): 6407-6414 (2017) - [j22]Aolin Xu, Maxim Raginsky:
Information-Theoretic Lower Bounds on Bayes Risk in Decentralized Estimation. IEEE Trans. Inf. Theory 63(3): 1580-1600 (2017) - [j21]Aolin Xu, Maxim Raginsky:
Information-Theoretic Lower Bounds for Distributed Function Computation. IEEE Trans. Inf. Theory 63(4): 2314-2337 (2017) - [c51]Ehsan Shafieepoorfard, Maxim Raginsky:
Rationally inattentive Markov decision processes over a finite horizon. ACSSC 2017: 621-627 - [c50]Naci Saldi, Tamer Basar, Maxim Raginsky:
Markov-Nash equilibria in mean-field games with discounted cost. ACC 2017: 3676-3681 - [c49]Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky:
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis. COLT 2017: 1674-1703 - [c48]Yanina Shkel, Maxim Raginsky, Sergio Verdú:
Universal lossy compression under logarithmic loss. ISIT 2017: 1157-1161 - [c47]Aolin Xu, Maxim Raginsky:
Information-theoretic analysis of generalization capability of learning algorithms. NIPS 2017: 2524-2533 - [i38]Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky:
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis. CoRR abs/1702.03849 (2017) - [i37]Naci Saldi, Tamer Basar, Maxim Raginsky:
Approximate Nash Equilibria in Partially Observed Stochastic Games with Mean-Field Interactions. CoRR abs/1705.02036 (2017) - [i36]Mehmet A. Donmez, Maxim Raginsky, Andrew C. Singer:
EE-Grad: Exploration and Exploitation for Cost-Efficient Mini-Batch SGD. CoRR abs/1705.07070 (2017) - [i35]Mehmet A. Donmez, Maxim Raginsky, Andrew C. Singer, Lav R. Varshney:
Cost-Performance Tradeoffs in Fusing Unreliable Computational Units. CoRR abs/1705.07779 (2017) - [i34]Aolin Xu, Maxim Raginsky:
Information-theoretic analysis of generalization capability of learning algorithms. CoRR abs/1705.07809 (2017) - [i33]Jaeho Lee, Maxim Raginsky:
Minimax Statistical Learning and Domain Adaptation with Wasserstein Distances. CoRR abs/1705.07815 (2017) - [i32]Ehsan Shafieepoorfard, Maxim Raginsky:
Sequential Empirical Coordination Under an Output Entropy Constraint. CoRR abs/1710.10255 (2017) - 2016
- [j20]Maxim Raginsky, Angelia Nedic:
Online Discrete Optimization in Social Networks in the Presence of Knightian Uncertainty. Oper. Res. 64(3): 662-679 (2016) - [j19]Mehmet A. Donmez, Maxim Raginsky, Andrew C. Singer:
Online Optimization Under Adversarial Perturbations. IEEE J. Sel. Top. Signal Process. 10(2): 256-269 (2016) - [j18]Ehsan Shafieepoorfard, Maxim Raginsky, Sean P. Meyn:
Rationally Inattentive Control of Markov Processes. SIAM J. Control. Optim. 54(2): 987-1016 (2016) - [j17]Maxim Raginsky:
Strong Data Processing Inequalities and Φ-Sobolev Inequalities for Discrete Channels. IEEE Trans. Inf. Theory 62(6): 3355-3389 (2016) - [c46]Mehmet A. Donmez, Maxim Raginsky, Andrew C. Singer, Lav R. Varshney:
Cost-performance tradeoffs in unreliable computation architectures. ACSSC 2016: 215-219 - [c45]Peng Guan, Maxim Raginsky, Rebecca Willett, Daphney-Stavroula Zois:
Regret minimization algorithms for single-controller zero-sum stochastic games. CDC 2016: 7075-7080 - [c44]Ehsan Shafieepoorfard, Maxim Raginsky:
Sequential empirical coordination under an output entropy constraint. CDC 2016: 7347-7352 - [c43]Maxim Raginsky:
Channel polarization and Blackwell measures. ISIT 2016: 56-60 - [c42]Maxim Raginsky, Alexander Rakhlin, Matthew Tsao, Yihong Wu, Aolin Xu:
Information-theoretic analysis of stability and bias of learning algorithms. ITW 2016: 26-30 - [c41]Daphney-Stavroula Zois, Maxim Raginsky:
Active object detection on graphs via locally informative trees. MLSP 2016: 1-6 - [i31]Aryeh Kontorovich, Maxim Raginsky:
Concentration of measure without independence: a unified approach via the martingale method. CoRR abs/1602.00721 (2016) - [i30]Aolin Xu, Maxim Raginsky:
Information-Theoretic Lower Bounds on Bayes Risk in Decentralized Estimation. CoRR abs/1607.00550 (2016) - [i29]Naci Saldi, Tamer Basar, Maxim Raginsky:
Markov-Nash Equilibria in Mean-Field Games with Discounted Cost. CoRR abs/1612.07878 (2016) - 2015
- [j16]Richard S. Laugesen, Prashant G. Mehta, Sean P. Meyn, Maxim Raginsky:
Poisson's Equation in Nonlinear Filtering. SIAM J. Control. Optim. 53(1): 501-525 (2015) - [c40]Angelia Nedic, Soomin Lee, Maxim Raginsky:
Decentralized online optimization with global objectives and local communication. ACC 2015: 4497-4503 - [c39]Aolin Xu, Maxim Raginsky:
Converses for distributed estimation via strong data processing inequalities. ISIT 2015: 2376-2380 - [c38]Jaeho Lee, Maxim Raginsky, Pierre Moulin:
On MMSE estimation from quantized observations in the nonasymptotic regime. ISIT 2015: 2924-2928 - [i28]Ehsan Shafieepoorfard, Maxim Raginsky, Sean P. Meyn:
Rationally inattentive control of Markov processes. CoRR abs/1502.03762 (2015) - [i27]Aolin Xu, Maxim Raginsky:
Converses for distributed estimation via strong data processing inequalities. CoRR abs/1504.06028 (2015) - [i26]Jaeho Lee, Maxim Raginsky, Pierre Moulin:
On MMSE estimation from quantized observations in the nonasymptotic regime. CoRR abs/1504.06029 (2015) - [i25]Soomin Lee, Angelia Nedic, Maxim Raginsky:
Decentralized Online Optimization with Global Objectives and Local Communication. CoRR abs/1508.07933 (2015) - [i24]Aolin Xu, Maxim Raginsky:
Information-theoretic lower bounds for distributed function computation. CoRR abs/1509.00514 (2015) - [i23]Maxim Raginsky, Igal Sason:
Concentration of Measure Inequalities and Their Communication and Information-Theoretic Applications. CoRR abs/1510.02947 (2015) - 2014
- [j15]Peng Guan, Maxim Raginsky, Rebecca M. Willett:
Online Markov Decision Processes With Kullback-Leibler Control Cost. IEEE Trans. Autom. Control. 59(6): 1423-1438 (2014) - [c37]Peng Guan, Maxim Raginsky, Rebecca Willett:
From minimax value to low-regret algorithms for online Markov decision processes. ACC 2014: 471-476 - [c36]Maxim Raginsky, Angelia Nedic:
Online discrete optimization in social networks. ACC 2014: 3796-3801 - [c35]Richard S. Laugesen, Prashant G. Mehta, Sean P. Meyn, Maxim Raginsky:
Poisson's equation in nonlinear filtering. CDC 2014: 4185-4190 - [c34]Aolin Xu, Maxim Raginsky:
A new information-theoretic lower bound for distributed function computation. ISIT 2014: 2227-2231 - [i22]Peng Guan, Maxim Raginsky, Rebecca Willett:
Online Markov decision processes with Kullback-Leibler control cost. CoRR abs/1401.3198 (2014) - [i21]Maxim Raginsky:
Strong data processing inequalities and $Φ$-Sobolev inequalities for discrete channels. CoRR abs/1411.3575 (2014) - 2013
- [j14]Maxim Raginsky, Igal Sason:
Concentration of Measure Inequalities in Information Theory, Communications, and Coding. Found. Trends Commun. Inf. Theory 10(1-2): 1-246 (2013) - [j13]Maxim Raginsky:
Empirical Processes, Typical Sequences, and Coordinated Actions in Standard Borel Spaces. IEEE Trans. Inf. Theory 59(3): 1288-1301 (2013) - [c33]Ehsan Shafieepoorfard, Maxim Raginsky, Sean P. Meyn:
Rational inattention in controlled Markov processes. ACC 2013: 6790-6797 - [c32]Ehsan Shafieepoorfard, Maxim Raginsky:
Rational inattention in scalar LQG control. CDC 2013: 5733-5739 - [c31]Maxim Raginsky, Igal Sason:
Refined bounds on the empirical distribution of good channel codes via concentration inequalities. ISIT 2013: 221-225 - [c30]Maxim Raginsky:
Logarithmic Sobolev inequalities and strong data processing theorems for discrete channels. ISIT 2013: 419-423 - [c29]Maxim Raginsky:
Learning joint quantizers for reconstruction and prediction. ITW 2013: 1-5 - [i20]Maxim Raginsky, Angelia Nedic:
Online discrete optimization in social networks in the presence of Knightian uncertainty. CoRR abs/1307.0473 (2013) - [i19]Peng Guan, Maxim Raginsky, Rebecca Willett:
Relax but stay in control: from value to algorithms for online Markov decision processes. CoRR abs/1310.7300 (2013) - 2012
- [j12]Kalyani Krishnamurthy, Rebecca Willett, Maxim Raginsky:
Target detection performance bounds in compressive imaging. EURASIP J. Adv. Signal Process. 2012: 205 (2012) - [j11]Maxim Raginsky, Rebecca M. Willett, Corinne Horn, Jorge G. Silva, Roummel F. Marcia:
Sequential Anomaly Detection in the Presence of Noise and Limited Feedback. IEEE Trans. Inf. Theory 58(8): 5544-5562 (2012) - [c28]Peng Guan, Maxim Raginsky, Rebecca Willett:
Online Markov decision processes with Kullback-Leibler control cost. ACC 2012: 1388-1393 - [c27]Maxim Raginsky, Jake V. Bouvrie:
Continuous-time stochastic Mirror Descent on a network: Variance reduction, consensus, convergence. CDC 2012: 6793-6800 - [i18]Maxim Raginsky, Igal Sason:
Concentration of Measure Inequalities in Information Theory, Communications and Coding. CoRR abs/1212.4663 (2012) - 2011
- [j10]Maxim Raginsky, Alexander Rakhlin:
Information-Based Complexity, Feedback and Dynamics in Convex Programming. IEEE Trans. Inf. Theory 57(10): 7036-7056 (2011) - [j9]Maxim Raginsky, Sina Jafarpour, Zachary T. Harmany, Roummel F. Marcia, Rebecca M. Willett, A. Robert Calderbank:
Performance Bounds for Expander-Based Compressed Sensing in Poisson Noise. IEEE Trans. Signal Process. 59(9): 4139-4153 (2011) - [c26]Maxim Raginsky:
Directed information and pearl's causal calculus. Allerton 2011: 958-965 - [c25]Maxim Raginsky, Nooshin Kiarashi, Rebecca Willett:
Decentralized Online Convex Programming with local information. ACC 2011: 5363-5369 - [c24]Maxim Raginsky:
Shannon meets Blackwell and Le Cam: Channels, codes, and statistical experiments. ISIT 2011: 1220-1224 - [c23]Maxim Raginsky, Alexander Rakhlin:
Lower Bounds for Passive and Active Learning. NIPS 2011: 1026-1034 - [i17]Maxim Raginsky:
Directed information and Pearl's causal calculus. CoRR abs/1110.0718 (2011) - 2010
- [j8]Kalyani Krishnamurthy, Maxim Raginsky, Rebecca Willett:
Multiscale Photon-Limited Spectral Image Reconstruction. SIAM J. Imaging Sci. 3(3): 619-645 (2010) - [j7]Maxim Raginsky, Rebecca Willett, Zachary T. Harmany, Roummel F. Marcia:
Compressed sensing performance bounds under Poisson noise. IEEE Trans. Signal Process. 58(8): 3990-4002 (2010) - [c22]Maxim Raginsky:
Divergence-based characterization of fundamental limitations of adaptive dynamical systems. Allerton 2010: 107-114 - [c21]Maxim Raginsky, Alexander Rakhlin, Serdar Yüksel:
Online Convex Programming and regularization in adaptive control. CDC 2010: 1957-1962 - [c20]Maxim Raginsky, Sina Jafarpour, Rebecca Willett, A. Robert Calderbank:
Fishing in Poisson streams: Focusing on the whales, ignoring the minnows. CISS 2010: 1-6 - [c19]Kalyani Krishnamurthy, Maxim Raginsky, Rebecca Willett:
Hyperspectral target detection from incoherent projections. ICASSP 2010: 3550-3553 - [c18]Kalyani Krishnamurthy, Maxim Raginsky, Rebecca Willett:
Hyperspectral target detection from incoherent projections: Nonequiprobable targets and inhomogeneous SNR. ICIP 2010: 1357-1360 - [c17]Todd P. Coleman, Maxim Raginsky:
Mutual information saddle points in channels of exponential family type. ISIT 2010: 1355-1359 - [c16]Maxim Raginsky:
Empirical processes and typical sequences. ISIT 2010: 1458-1462 - [i16]Maxim Raginsky, Sina Jafarpour, Rebecca Willett, A. Robert Calderbank:
Fishing in Poisson streams: focusing on the whales, ignoring the minnows. CoRR abs/1003.2836 (2010) - [i15]Maxim Raginsky, Sina Jafarpour, Zachary T. Harmany, Roummel F. Marcia, Rebecca Willett, A. Robert Calderbank:
Performance bounds for expander-based compressed sensing in Poisson noise. CoRR abs/1007.2377 (2010) - [i14]Maxim Raginsky:
Empirical processes, typical sequences and coordinated actions in standard Borel spaces. CoRR abs/1009.0282 (2010) - [i13]Maxim Raginsky, Alexander Rakhlin:
Information-based complexity, feedback and dynamics in sequential convex programming. CoRR abs/1010.2285 (2010) - [i12]Maxim Raginsky:
Divergence-based characterization of fundamental limitations of adaptive dynamical systems. CoRR abs/1010.2286 (2010)
2000 – 2009
- 2009
- [j6]Svetlana Lazebnik, Maxim Raginsky:
Supervised Learning of Quantizer Codebooks by Information Loss Minimization. IEEE Trans. Pattern Anal. Mach. Intell. 31(7): 1294-1309 (2009) - [j5]Maxim Raginsky:
Joint universal lossy coding and identification of stationary mixing sources with general alphabets. IEEE Trans. Inf. Theory 55(5): 1945-1960 (2009) - [j4]Avon L. Fernandes, Maxim Raginsky, Todd P. Coleman:
A low-complexity universal scheme for rate-constrained distributed regression using a wireless sensor network. IEEE Trans. Signal Process. 57(5): 1731-1744 (2009) - [c15]Maxim Raginsky, Alexander Rakhlin:
Information complexity of black-box convex optimization: A new look via feedback information theory. Allerton 2009: 803-510 - [c14]Svetlana Lazebnik, Maxim Raginsky:
An empirical Bayes approach to contextual region classification. CVPR 2009: 2380-2387 - [c13]Rebecca M. Willett, Maxim Raginsky:
Performance bounds on compressed sensing with Poisson noise. ISIT 2009: 174-178 - [c12]Maxim Raginsky:
Achievability results for statistical learning under communication constraints. ISIT 2009: 1328-1332 - [c11]Maxim Raginsky, Roummel F. Marcia, Jorge G. Silva, Rebecca M. Willett:
Sequential probability assignment via online convex programming using exponential families. ISIT 2009: 1338-1342 - [c10]Maxim Raginsky, Svetlana Lazebnik:
Locality-sensitive binary codes from shift-invariant kernels. NIPS 2009: 1509-1517 - [i11]Rebecca Willett, Maxim Raginsky:
Minimax risk for Poisson compressed sensing. CoRR abs/0901.1900 (2009) - [i10]Maxim Raginsky:
Joint universal lossy coding and identification of stationary mixing sources with general alphabets. CoRR abs/0901.1904 (2009) - [i9]Maxim Raginsky:
Achievability results for statistical learning under communication constraints. CoRR abs/0901.1905 (2009) - [i8]Maxim Raginsky, Zachary T. Harmany, Roummel F. Marcia, Rebecca Willett:
Compressed sensing performance bounds under Poisson noise. CoRR abs/0910.5146 (2009) - [i7]Sina Jafarpour, Rebecca Willett, Maxim Raginsky, A. Robert Calderbank:
Performance Bounds for Expander-based Compressed Sensing in the presence of Poisson Noise. CoRR abs/0911.1368 (2009) - [i6]Maxim Raginsky, Roummel F. Marcia, Jorge G. Silva, Rebecca Willett:
Sequential anomaly detection in the presence of noise and limited feedback. CoRR abs/0911.2904 (2009) - 2008
- [j3]Maxim Raginsky, Thomas J. Anastasio:
Cooperation in self-organizing map networks enhances information transmission in the presence of input background activity. Biol. Cybern. 98(3): 195-211 (2008) - [j2]Maxim Raginsky:
Joint Fixed-Rate Universal Lossy Coding and Identification of Continuous-Alphabet Memoryless Sources. IEEE Trans. Inf. Theory 54(7): 3059-3077 (2008) - [c9]Maxim Raginsky:
On the information capacity of Gaussian channels under small peak power constraints. Allerton 2008: 286-293 - [c8]Avon L. Fernandes, Maxim Raginsky, Todd P. Coleman:
A low-complexity universal scheme for rate-constrained distributed regression using a wireless sensor network. ICASSP 2008: 2269-2272 - [c7]Maxim Raginsky:
Universal Wyner-Ziv coding of discrete memoryless sources with known side information statistics. ISIT 2008: 2167-2171 - [c6]Maxim Raginsky, Svetlana Lazebnik, Rebecca Willett, Jorge G. Silva:
Near-minimax recursive density estimation on the binary hypercube. NIPS 2008: 1305-1312 - 2007
- [c5]Maxim Raginsky:
Joint Universal Lossy Coding and Identification of Stationary Mixing Sources. ISIT 2007: 1961-1965 - [c4]Svetlana Lazebnik, Maxim Raginsky:
Learning Nearest-Neighbor Quantizers from Labeled Data by Information Loss Minimization. AISTATS 2007: 251-258 - [i5]Maxim Raginsky:
Learning from compressed observations. CoRR abs/0704.0671 (2007) - [i4]Maxim Raginsky:
Joint universal lossy coding and identification of stationary mixing sources. CoRR abs/0704.2644 (2007) - 2006
- [c3]Maxim Raginsky:
Joint Universal Lossy Coding and Identification of I.I.D. Vector Sources. ISIT 2006: 577-581 - [i3]Maxim Raginsky:
Joint universal lossy coding and identification of i.i.d. vector sources. CoRR abs/cs/0601074 (2006) - 2005
- [c2]Maxim Raginsky:
A complexity-regularized quantization approach to nonlinear dimensionality reduction. ISIT 2005: 352-356 - [c1]Maxim Raginsky, Svetlana Lazebnik:
Estimation of Intrinsic Dimensionality Using High-Rate Vector Quantization. NIPS 2005: 1105-1112 - [i2]Maxim Raginsky:
A complexity-regularized quantization approach to nonlinear dimensionality reduction. CoRR abs/cs/0501091 (2005) - [i1]Maxim Raginsky:
Joint fixed-rate universal lossy coding and identification of continuous-alphabet memoryless sources. CoRR abs/cs/0512015 (2005) - 2003
- [j1]Maxim Raginsky:
Scaling and Renormalization in Fault-Tolerant Quantum Computers. Quantum Inf. Process. 2(3): 249-258 (2003)
Coauthor Index
Rebecca Willett
aka: Rebecca M. Willett
aka: Rebecca M. Willett
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