Maxim Raginsky

ORCID

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 90

Top Venues

CoRR

CDC

ISIT

IEEE Trans. Inf. Theory

Publications

Joshua Hanson, Maxim Raginsky
Rademacher complexity of neural ODEs via Chen-Fliess series. L4DC (2024)
Joshua Hanson, Maxim Raginsky
Rademacher Complexity of Neural ODEs via Chen-Fliess Series. CoRR (2024)
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. Trans. Mach. Learn. Res. 2024 (2024)
Tanya Veeravalli, Maxim Raginsky
Revisiting Stochastic Realization Theory using Functional Itô Calculus. CoRR (2024)
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 (2024)
Yifeng Chu, Maxim Raginsky
Majorizing Measures, Codes, and Information. CoRR (2023)
Belinda Tzen, Anant Raj, Maxim Raginsky, Francis R. Bach
Variational Principles for Mirror Descent and Mirror Langevin Dynamics. IEEE Control. Syst. Lett. 7 (2023)
Yifeng Chu, Maxim Raginsky
A unified framework for information-theoretic generalization bounds. NeurIPS (2023)
Naci Saldi, Tamer Basar, Maxim Raginsky
Partially Observed Discrete-Time Risk-Sensitive Mean Field Games. Dyn. Games Appl. 13 (3) (2023)
Yifeng Chu, Maxim Raginsky
A unified framework for information-theoretic generalization bounds. CoRR (2023)
Yifeng Chu, Maxim Raginsky
A Chain Rule for the Expected Suprema of Bernoulli Processes. CoRR (2023)
Tanya Veeravalli, Maxim Raginsky
A Constructive Approach to Function Realization by Neural Stochastic Differential Equations. CDC (2023)
Fredrik Hellström, Giuseppe Durisi, Benjamin Guedj, Maxim Raginsky
Generalization Bounds: Perspectives from Information Theory and PAC-Bayes. CoRR (2023)
Shizhuo Dylan Zhang, Curt Tigges, Stella Biderman, Maxim Raginsky, Talia Ringer
Can Transformers Learn to Solve Problems Recursively? CoRR (2023)
Tanya Veeravalli, Maxim Raginsky
A Constructive Approach to Function Realization by Neural Stochastic Differential Equations. CoRR (2023)
Yifeng Chu, Maxim Raginsky
Majorizing Measures, Codes, and Information. ISIT (2023)
Belinda Tzen, Anant Raj, Maxim Raginsky, Francis R. Bach
Variational Principles for Mirror Descent and Mirror Langevin Dynamics. CoRR (2023)
Tanya Veeravalli, Maxim Raginsky
Nonlinear Controllability and Function Representation by Neural Stochastic Differential Equations. L4DC (2023)
Tanya Veeravalli, Maxim Raginsky
Nonlinear controllability and function representation by neural stochastic differential equations. CoRR (2022)
Joshua Hanson, Maxim Raginsky
Fitting an immersed submanifold to data via Sussmann's orbit theorem. CDC (2022)
Joshua Hanson, Maxim Raginsky
Fitting an immersed submanifold to data via Sussmann's orbit theorem. CoRR (2022)
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)
Alan Yang, Jie Xiong, Maxim Raginsky, Elyse Rosenbaum
Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits. CoRR (2022)
Aolin Xu, Maxim Raginsky
Minimum Excess Risk in Bayesian Learning. IEEE Trans. Inf. Theory 68 (12) (2022)
Ali Devran Kara, Maxim Raginsky, Serdar Yüksel
Robustness to incorrect models and data-driven learning in average-cost optimal stochastic control. Autom. 139 (2022)
Hrayr Harutyunyan, Maxim Raginsky, Greg Ver Steeg, Aram Galstyan
Information-theoretic generalization bounds for black-box learning algorithms. CoRR (2021)
Jie Xiong, Alan Yang, Maxim Raginsky, Elyse Rosenbaum
Neural Networks for Transient Modeling of Circuits : Invited Paper. MLCAD (2021)
Mehmet A. Donmez, Jeff Ludwig, Maxim Raginsky, Andrew C. Singer
EE-Grad: Exploration and Exploitation for Cost-Efficient Mini-Batch SGD. ACSCC (2021)
Todd P. Coleman, Maxim Raginsky
Sampling, variational Bayesian inference, and conditioned stochastic differential equations. CDC (2021)
Joshua Hanson, Maxim Raginsky, Eduardo D. Sontag
Learning Recurrent Neural Net Models of Nonlinear Systems. L4DC (2021)
Hrayr Harutyunyan, Maxim Raginsky, Greg Ver Steeg, Aram Galstyan
Information-theoretic generalization bounds for black-box learning algorithms. NeurIPS (2021)
Aolin Xu, Maxim Raginsky
Minimum Excess Risk in Bayesian Learning. CoRR (2020)
Alan Yang, AmirEmad Ghassami, Maxim Raginsky, Negar Kiyavash, Elyse Rosenbaum
Model-Augmented Conditional Mutual Information Estimation for Feature Selection. UAI (2020)
Joshua Hanson, Maxim Raginsky, Eduardo D. Sontag
Learning Recurrent Neural Net Models of Nonlinear Systems. CoRR (2020)
Belinda Tzen, Maxim Raginsky
A mean-field theory of lazy training in two-layer neural nets: entropic regularization and controlled McKean-Vlasov dynamics. CoRR (2020)
Naveen Goela, Maxim Raginsky
Channel Polarization Through the Lens of Blackwell Measures. IEEE Trans. Inf. Theory 66 (10) (2020)
Naci Saldi, Tamer Basar, Maxim Raginsky
Partially Observed Discrete-Time Risk-Sensitive Mean Field Games. CoRR (2020)
Naci Saldi, Tamer Basar, Maxim Raginsky
Approximate Markov-Nash Equilibria for Discrete-Time Risk-Sensitive Mean-Field Games. Math. Oper. Res. 45 (4) (2020)
Joshua Hanson, Maxim Raginsky
Universal Simulation of Stable Dynamical Systems by Recurrent Neural Nets. L4DC (2020)
Belinda Tzen, Maxim Raginsky
Theoretical guarantees for sampling and inference in generative models with latent diffusions. COLT (2019)
Naci Saldi, Tamer Basar, Maxim Raginsky
Partially-Observed Discrete-Time Risk-Sensitive Mean-Field Games. CDC (2019)
Ali Devran Kara, Maxim Raginsky, Serdar Yüksel
Robustness to Incorrect Models in Average-Cost Optimal Stochastic Control. CDC (2019)
Belinda Tzen, Maxim Raginsky
Theoretical guarantees for sampling and inference in generative models with latent diffusions. CoRR (2019)
Belinda Tzen, Maxim Raginsky
Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit. CoRR (2019)
Jaeho Lee, Maxim Raginsky
Learning Finite-Dimensional Coding Schemes with Nonlinear Reconstruction Maps. SIAM J. Math. Data Sci. 1 (3) (2019)
Alan Yang, AmirEmad Ghassami, Maxim Raginsky, Negar Kiyavash, Elyse Rosenbaum
Model-Augmented Nearest-Neighbor Estimation of Conditional Mutual Information for Feature Selection. CoRR (2019)
Naci Saldi, Tamer Basar, Maxim Raginsky
Approximate Nash Equilibria in Partially Observed Stochastic Games with Mean-Field Interactions. Math. Oper. Res. 44 (3) (2019)
Joshua Hanson, Maxim Raginsky
Universal Approximation of Input-Output Maps by Temporal Convolutional Nets. NeurIPS (2019)
Noyan C. Sevüktekin, Maxim Raginsky, Andrew C. Singer
Linear Noisy Networks with Stochastic Components. CDC (2019)
Joshua Hanson, Maxim Raginsky
Universal Approximation of Input-Output Maps by Temporal Convolutional Nets. CoRR (2019)
Belinda Tzen, Tengyuan Liang, Maxim Raginsky
Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability. COLT (2018)
Naci Saldi, Tamer Basar, Maxim Raginsky
Markov-Nash Equilibria in Mean-Field Games with Discounted Cost. SIAM J. Control. Optim. 56 (6) (2018)
Naveen Goela, Maxim Raginsky
Channel Polarization through the Lens of Blackwell Measures. CoRR (2018)
Yanina Shkel, Maxim Raginsky, Sergio Verdú
Sequential prediction with coded side information under logarithmic loss. ALT (2018)
Jaeho Lee, Maxim Raginsky
Learning finite-dimensional coding schemes with nonlinear reconstruction maps. CoRR (2018)
Naci Saldi, Tamer Basar, Maxim Raginsky
Discrete-time Risk-sensitive Mean-field Games. CoRR (2018)
Soomin Lee, Angelia Nedic, Maxim Raginsky
Coordinate Dual Averaging for Decentralized Online Optimization With Nonseparable Global Objectives. IEEE Trans. Control. Netw. Syst. 5 (1) (2018)
Ehsan Shafieepoorfard, Maxim Raginsky
Sequential Empirical Coordination Under an Output Entropy Constraint. IEEE Trans. Inf. Theory 64 (10) (2018)
Yanina Shkel, Maxim Raginsky, Sergio Verdú
Universal Compression, List Decoding, and Logarithmic Loss. ISIT (2018)
Jaeho Lee, Maxim Raginsky
Minimax Statistical Learning with Wasserstein distances. NeurIPS (2018)
Belinda Tzen, Tengyuan Liang, Maxim Raginsky
Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability. CoRR (2018)
Yang Xiu, Samuel Sagan, Advika Battini, Xiao Ma, Maxim Raginsky, Elyse Rosenbaum
Stochastic modeling of air electrostatic discharge parameters. IRPS (2018)
Naci Saldi, Tamer Basar, Maxim Raginsky
Approximate Nash Equilibria in Partially Observed Stochastic Games with Mean-Field Interactions. CoRR (2017)
Mehmet A. Donmez, Maxim Raginsky, Andrew C. Singer, Lav R. Varshney
Cost-Performance Tradeoffs in Fusing Unreliable Computational Units. CoRR (2017)
Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis. COLT (2017)
Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis. CoRR (2017)
Yanina Shkel, Maxim Raginsky, Sergio Verdú
Universal lossy compression under logarithmic loss. ISIT (2017)
Aolin Xu, Maxim Raginsky
Information-Theoretic Lower Bounds on Bayes Risk in Decentralized Estimation. IEEE Trans. Inf. Theory 63 (3) (2017)
Ehsan Shafieepoorfard, Maxim Raginsky
Sequential Empirical Coordination Under an Output Entropy Constraint. CoRR (2017)
Aolin Xu, Maxim Raginsky
Information-Theoretic Lower Bounds for Distributed Function Computation. IEEE Trans. Inf. Theory 63 (4) (2017)
Naci Saldi, Tamer Basar, Maxim Raginsky
Markov-Nash equilibria in mean-field games with discounted cost. ACC (2017)
Ehsan Shafieepoorfard, Maxim Raginsky
Rationally inattentive Markov decision processes over a finite horizon. ACSSC (2017)
Soomin Lee, Angelia Nedic, Maxim Raginsky
Stochastic Dual Averaging for Decentralized Online Optimization on Time-Varying Communication Graphs. IEEE Trans. Autom. Control. 62 (12) (2017)
Aolin Xu, Maxim Raginsky
Information-theoretic analysis of generalization capability of learning algorithms. NIPS (2017)
Mehmet A. Donmez, Maxim Raginsky, Andrew C. Singer
EE-Grad: Exploration and Exploitation for Cost-Efficient Mini-Batch SGD. CoRR (2017)
Jaeho Lee, Maxim Raginsky
Minimax Statistical Learning and Domain Adaptation with Wasserstein Distances. CoRR (2017)
Aolin Xu, Maxim Raginsky
Information-theoretic analysis of generalization capability of learning algorithms. CoRR (2017)
Maxim Raginsky
Channel polarization and Blackwell measures. ISIT (2016)
Aryeh Kontorovich, Maxim Raginsky
Concentration of measure without independence: a unified approach via the martingale method. CoRR (2016)
Daphney-Stavroula Zois, Maxim Raginsky
Active object detection on graphs via locally informative trees. MLSP (2016)
Maxim Raginsky, Angelia Nedic
Online Discrete Optimization in Social Networks in the Presence of Knightian Uncertainty. Oper. Res. 64 (3) (2016)
Maxim Raginsky
Strong Data Processing Inequalities and Φ-Sobolev Inequalities for Discrete Channels. IEEE Trans. Inf. Theory 62 (6) (2016)
Ehsan Shafieepoorfard, Maxim Raginsky
Sequential empirical coordination under an output entropy constraint. CDC (2016)
Ehsan Shafieepoorfard, Maxim Raginsky, Sean P. Meyn
Rationally Inattentive Control of Markov Processes. SIAM J. Control. Optim. 54 (2) (2016)
Naci Saldi, Tamer Basar, Maxim Raginsky
Markov-Nash Equilibria in Mean-Field Games with Discounted Cost. CoRR (2016)
Mehmet A. Donmez, Maxim Raginsky, Andrew C. Singer, Lav R. Varshney
Cost-performance tradeoffs in unreliable computation architectures. ACSSC (2016)
Peng Guan, Maxim Raginsky, Rebecca Willett, Daphney-Stavroula Zois
Regret minimization algorithms for single-controller zero-sum stochastic games. CDC (2016)
Mehmet A. Donmez, Maxim Raginsky, Andrew C. Singer
Online Optimization Under Adversarial Perturbations. IEEE J. Sel. Top. Signal Process. 10 (2) (2016)
Aolin Xu, Maxim Raginsky
Information-Theoretic Lower Bounds on Bayes Risk in Decentralized Estimation. CoRR (2016)
Maxim Raginsky, Alexander Rakhlin, Matthew Tsao, Yihong Wu, Aolin Xu
Information-theoretic analysis of stability and bias of learning algorithms. ITW (2016)
Aolin Xu, Maxim Raginsky
Information-theoretic lower bounds for distributed function computation. CoRR (2015)
Aolin Xu, Maxim Raginsky
Converses for distributed estimation via strong data processing inequalities. CoRR (2015)
Ehsan Shafieepoorfard, Maxim Raginsky, Sean P. Meyn
Rationally inattentive control of Markov processes. CoRR (2015)
Jaeho Lee, Maxim Raginsky, Pierre Moulin
On MMSE estimation from quantized observations in the nonasymptotic regime. ISIT (2015)
Maxim Raginsky, Igal Sason
Concentration of Measure Inequalities and Their Communication and Information-Theoretic Applications. CoRR (2015)
Aolin Xu, Maxim Raginsky
Converses for distributed estimation via strong data processing inequalities. ISIT (2015)
Soomin Lee, Angelia Nedic, Maxim Raginsky
Decentralized Online Optimization with Global Objectives and Local Communication. CoRR (2015)
Jaeho Lee, Maxim Raginsky, Pierre Moulin
On MMSE estimation from quantized observations in the nonasymptotic regime. CoRR (2015)
Richard S. Laugesen, Prashant G. Mehta, Sean P. Meyn, Maxim Raginsky
Poisson's Equation in Nonlinear Filtering. SIAM J. Control. Optim. 53 (1) (2015)
Angelia Nedic, Soomin Lee, Maxim Raginsky
Decentralized online optimization with global objectives and local communication. ACC (2015)

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Publication Activity (10 Years)

Top Topics

Top Venues

Maxim Raginsky