Alex A. Gorodetsky
Orcid: 0000-0003-3152-8206Affiliations:
- University of Michigan, USA
According to our database1,
Alex A. Gorodetsky
authored at least 33 papers
between 2014 and 2024.
Collaborative distances:
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Bibliography
2024
Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling.
CoRR, 2024
2023
High-dimensional data analytics in civil engineering: A review on matrix and tensor decomposition.
Eng. Appl. Artif. Intell., 2023
2022
Ensemble Approximate Control Variate Estimators: Applications to MultiFidelity Importance Sampling.
SIAM/ASA J. Uncertain. Quantification, March, 2022
IEEE Trans. Control. Netw. Syst., 2022
Neural Comput. Appl., 2022
Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning.
J. Mach. Learn. Res., 2022
Robust identification of non-autonomous dynamical systems using stochastic dynamics models.
CoRR, 2022
Bayesian Identification of Nonseparable Hamiltonian Systems Using Stochastic Dynamic Models.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022
2021
A New Objective for Identification of Partially Observed Linear Time-Invariant Dynamical Systems from Input-Output Data.
Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021
Bayesian Inference for Time Delay Systems with Application to Connected Automated Vehicles.
Proceedings of the 24th IEEE International Intelligent Transportation Systems Conference, 2021
Behavioral Cloning in Atari Games Using a Combined Variational Autoencoder and Predictor Model.
Proceedings of the IEEE Congress on Evolutionary Computation, 2021
2020
A generalized approximate control variate framework for multifidelity uncertainty quantification.
J. Comput. Phys., 2020
CoRR, 2020
Efficient MCMC Sampling for Bayesian Matrix Factorization by Breaking Posterior Symmetries.
CoRR, 2020
Bayesian System ID: Optimal management of parameter, model, and measurement uncertainty.
CoRR, 2020
Proceedings of the 59th IEEE Conference on Decision and Control, 2020
Uncertainty Quantification Using Generalized Polynomial Chaos for Online Simulations of Automotive Propulsion Systems.
Proceedings of the 2020 American Control Conference, 2020
2019
Adaptive Multi-index Collocation for Uncertainty Quantification and Sensitivity Analysis.
CoRR, 2019
Randomized Functional Sparse Tucker Tensor for Compression and Fast Visualization of Scientific Data.
CoRR, 2019
2018
SIAM J. Sci. Comput., 2018
J. Comput. Phys., 2018
Int. J. Robotics Res., 2018
Visual-Inertial Navigation Algorithm Development Using Photorealistic Camera Simulation in the Loop.
Proceedings of the 2018 IEEE International Conference on Robotics and Automation, 2018
Continuous Tensor Train-Based Dynamic Programming for High-Dimensional Zero-Sum Differential Games.
Proceedings of the 2018 Annual American Control Conference, 2018
2017
Low-rank tensor integration for Gaussian filtering of continuous time nonlinear systems.
Proceedings of the 56th IEEE Annual Conference on Decision and Control, 2017
2016
Mercer Kernels and Integrated Variance Experimental Design: Connections Between Gaussian Process Regression and Polynomial Approximation.
SIAM/ASA J. Uncertain. Quantification, 2016
Automated synthesis of low-rank control systems from sc-LTL specifications using tensor-train decompositions.
Proceedings of the 55th IEEE Conference on Decision and Control, 2016
2015
Efficient High-Dimensional Stochastic Optimal Motion Control using Tensor-Train Decomposition.
Proceedings of the Robotics: Science and Systems XI, Sapienza University of Rome, 2015
2014
SIAM J. Sci. Comput., 2014