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08/31/2023
Dropout Ensemble Kalman inversion for high dimensional inverse problems
Ensemble Kalman inversion (EKI) is an ensemble-based method to solve inv...
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06/22/2023
PCA matrix denoising is uniform
Principal component analysis (PCA) is a simple and popular tool for proc...
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10/12/2022
Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent
Sampling methods, as important inference and learning techniques, are ty...
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05/17/2022
Can We Do Better Than Random Start? The Power of Data Outsourcing
Many organizations have access to abundant data but lack the computation...
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03/07/2022
Convergence Speed and Approximation Accuracy of Numerical MCMC
When implementing Markov Chain Monte Carlo (MCMC) algorithms, perturbati...
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02/06/2022
Stochastic Gradient Descent with Dependent Data for Offline Reinforcement Learning
In reinforcement learning (RL), offline learning decoupled learning from...
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01/26/2022
Localization in Ensemble Kalman inversion
Ensemble Kalman inversion (EKI) is a technique for the numerical solutio...
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10/18/2021
Adaptive Tikhonov strategies for stochastic ensemble Kalman inversion
Ensemble Kalman inversion (EKI) is a derivative-free optimizer aimed at ...
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01/07/2021
A unified performance analysis of likelihood-informed subspace methods
The likelihood-informed subspace (LIS) method offers a viable route to r...
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07/06/2020
Consistency analysis of bilevel data-driven learning in inverse problems
One fundamental problem when solving inverse problems is how to find reg...
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06/29/2020
Spectral Gap of Replica Exchange Langevin Diffusion on Mixture Distributions
Langevin diffusion (LD) is one of the main workhorses for sampling probl...
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03/25/2020
Dimension Independent Generalization Error with Regularized Online Optimization
One classical canon of statistics is that large models are prone to over...
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01/23/2020
Replica Exchange for Non-Convex Optimization
Gradient descent (GD) is known to converge quickly for convex objective ...
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11/06/2019
Convergence Acceleration of Ensemble Kalman Inversion in Nonlinear Settings
Many data-science problems can be formulated as an inverse problem, wher...
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06/23/2019
Accelerating Metropolis-within-Gibbs sampler with localized computations of differential equations
Inverse problem is ubiquitous in science and engineering, and Bayesian m...
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04/30/2019
Hitting Time of Stochastic Gradient Langevin Dynamics to Stationary Points: A Direct Analysis
Stochastic gradient Langevin dynamics (SGLD) is a fundamental algorithm ...
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01/17/2019
Spatial localization for nonlinear dynamical stochastic models for excitable media
Nonlinear dynamical stochastic models are ubiquitous in different areas....
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10/27/2016