Maxim Raginsky
20 papers · 2008–2026 · 5 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Interdisciplinary Bridge π Renaissance Researcher (5) πΊοΈ Taxonomy Completionist (13) π£ Hot Topic Early Bird
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Conference Polyglot
(5)
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π
Keyword Champion
ποΈ
Keyword Collector
(94)
π
Trend Setter
π
Century Club
(19)
π₯
Unstoppable
(8)
π
Conference Pioneer
Conferences
NIPS (8)
L4DC (5)
COLT (4)
ALT (2)
UAI (1)
Top co-authors
Research topics
Keywords
information theory
(4)
generalization bound
(3)
neural network
(3)
learning theory
(3)
non-convex optimization
(2)
empirical risk minimization
(2)
recurrent neural network
(2)
mutual information
(2)
stochastic process
(2)
function approximation
(1)
feature selection
(1)
density estimation
(1)
statistical learning
(1)
optimal transport
(1)
similarity search
(1)
domain adaptation
(1)
motion planning
(1)
probability estimation
(1)
gaussian process
(1)
variational inference
(1)
Papers
Talagrand Meets Talagrand: Upper and Lower Bounds on Expected Soft Maxima of Gaussian Processes with Finite Index Sets
ALT 2026
Rademacher complexity of neural ODEs via Chen-Fliess series
L4DC 2024
A unified framework for information-theoretic generalization bounds
NIPS 2023
Nonlinear Controllability and Function Representation by Neural Stochastic Differential Equations
L4DC 2023
Conference on Learning Theory 2022: Preface
COLT 2022
Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits
L4DC 2022
Information-theoretic generalization bounds for black-box learning algorithms
NIPS 2021
Learning Recurrent Neural Net Models of Nonlinear Systems
L4DC 2021
Model-Augmented Conditional Mutual Information Estimation for Feature Selection
UAI 2020
Universal Simulation of Stable Dynamical Systems by Recurrent Neural Nets
L4DC 2020
Universal Approximation of Input-Output Maps by Temporal Convolutional Nets
NIPS 2019
Theoretical guarantees for sampling and inference in generative models with latent diffusions
COLT 2019
Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability
COLT 2018
Minimax Statistical Learning with Wasserstein distances
NIPS 2018
Sequential prediction with coded side information under logarithmic loss
ALT 2018
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis
COLT 2017
Information-theoretic analysis of generalization capability of learning algorithms
NIPS 2017
Lower Bounds for Passive and Active Learning
NIPS 2011
Locality-sensitive binary codes from shift-invariant kernels
NIPS 2009
Near-minimax recursive density estimation on the binary hypercube
NIPS 2008