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05/18/2023
Small noise analysis for Tikhonov and RKHS regularizations
Regularization plays a pivotal role in ill-posed machine learning and in...
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03/24/2023
An adaptive RKHS regularization for Fredholm integral equations
Regularization is a long-standing challenge for ill-posed linear inverse...
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01/30/2023
Benchmarking optimality of time series classification methods in distinguishing diffusions
Performance benchmarking is a crucial component of time series classific...
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12/29/2022
A Data-Adaptive Prior for Bayesian Learning of Kernels in Operators
Kernels are efficient in representing nonlocal dependence and they are w...
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09/06/2022
Stochastic Data-Driven Variational Multiscale Reduced Order Models
Trajectory-wise data-driven reduced order models (ROMs) tend to be sensi...
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07/13/2022
NySALT: Nyström-type inference-based schemes adaptive to large time-stepping
Large time-stepping is important for efficient long-time simulations of ...
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07/12/2022
Unsupervised learning of observation functions in state-space models by nonparametric moment methods
We investigate the unsupervised learning of non-invertible observation f...
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05/23/2022
Nonparametric learning of kernels in nonlocal operators
Nonlocal operators with integral kernels have become a popular tool for ...
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03/08/2022
Data adaptive RKHS Tikhonov regularization for learning kernels in operators
We present DARTR: a Data Adaptive RKHS Tikhonov Regularization method fo...
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12/27/2021
Shock trace prediction by reduced models for a viscous stochastic Burgers equation
Viscous shocks are a particular type of extreme events in nonlinear mult...
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06/10/2021
Identifiability of interaction kernels in mean-field equations of interacting particles
We study the identifiability of the interaction kernels in mean-field eq...
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04/12/2021
Domain Adaptive Monocular Depth Estimation With Semantic Information
The advent of deep learning has brought an impressive advance to monocul...
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02/25/2021
ISALT: Inference-based schemes adaptive to large time-stepping for locally Lipschitz ergodic systems
Efficient simulation of SDEs is essential in many applications, particul...
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11/20/2020
On the coercivity condition in the learning of interacting particle systems
In the learning of systems of interacting particles or agents, coercivit...
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10/29/2020
Learning interaction kernels in mean-field equations of 1st-order systems of interacting particles
We introduce a nonparametric algorithm to learn interaction kernels of m...
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10/02/2020
Data-driven model reduction for stochastic Burgers equations
We present a class of efficient parametric closure models for 1D stochas...
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07/30/2020
Learning interaction kernels in stochastic systems of interacting particles from multiple trajectories
We consider stochastic systems of interacting particles or agents, with ...
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07/04/2020
Cluster Prediction for Opinion Dynamics from Partial Observations
We present a Bayesian approach to predict the clustering of opinions for...
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12/27/2019
On the identifiability of interaction functions in systems of interacting particles
Identifiability is of fundamental importance in the statistical learning...
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10/10/2019
Learning interaction kernels in heterogeneous systems of agents from multiple trajectories
Systems of interacting particles or agents have wide applications in man...
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08/21/2019
Data-driven model reduction, Wiener projections, and the Mori-Zwanzig formalism
First-principles models of complex dynamic phenomena often have many deg...
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04/10/2019
Joint state-parameter estimation of a nonlinear stochastic energy balance model from sparse noisy data
While nonlinear stochastic partial differential equations arise naturall...
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03/11/2019
The Unconstrained Ear Recognition Challenge 2019 - ArXiv version With Appendix
This paper presents a summary of the 2019 Unconstrained Ear Recognition ...
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03/11/2019
The Unconstrained Ear Recognition Challenge 2019
This paper presents a summary of the 2019 Unconstrained Ear Recognition ...
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12/14/2018