1 citations · 1 across the 9 of their papers we have counts for
9 papers
Physics Matters in PnP: Recovery Guarantees with the MMSE and NN Denoisers
Tobias Wolf, Jalal Fadili, Jin Guo +1
We investigate the forward-backward-splitting version of the Plug and Play (PnP) method for linear ill-posed problems with MMSE estimators as denoisers. In contrast to existing lit…
Second-Order Path Kernel Interpolation Formulas in Machine Learning
Jin Guo, Roy Y. He, Jean-Michel Morel
Understanding how training data shape neural network predictions is a central problem in modern learning theory. In 2020, Pedro Domingos proposed an interpolation formula valid for…
On Interpolation Formulas Describing Neural Network Generalization
Jin Guo, Roy Y. He, Jean-Michel Morel
In 2020 Domingos introduced an interpolation formula valid for "every model trained by gradient descent". He concluded that such models behave approximately as kernel machines. In…
Action Functional as an Early Warning Indicator in the Space of Probability Measures via Schrödinger Bridge
Peng Zhang, Ting Gao, Jin Guo +1
Critical transitions and tipping phenomena between two meta-stable states in stochastic dynamical systems are a significant scientific issue. In this work, we expand the methodolog…
Early Warning Prediction with Automatic Labeling in Epilepsy Patients
Peng Zhang, Ting Gao, Jin Guo +2
Early warning for epilepsy patients is crucial for their safety and well-being, in particular to prevent or minimize the severity of seizures. Through the patients' EEG data, we pr…
Efficient Privacy-Preserving Convolutional Spiking Neural Networks with FHE
Pengbo Li, Huifang Huang, Ting Gao +2
With the rapid development of AI technology, we have witnessed numerous innovations and conveniences. However, along with these advancements come privacy threats and risks. Fully H…
Learning Stochastic Dynamical Systems as an Implicit Regularization with Graph Neural Networks
Jin Guo, Ting Gao, Yufu Lan +3
Stochastic Gumbel graph networks are proposed to learn high-dimensional time series, where the observed dimensions are often spatially correlated. To that end, the observed randomn…
Deep Reinforcement Learning in Finite-Horizon to Explore the Most Probable Transition Pathway
Jin Guo, Ting Gao, Peng Zhang +2
In many scientific and engineering problems, noise and nonlinearity are unavoidable, which could induce interesting mathematical problem such as transition phenomena. This paper fo…
Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization
Huifang Huang, Ting Gao, Pengbo Li +3
With the fast development of quantitative portfolio optimization in financial engineering, lots of AI-based algorithmic trading strategies have demonstrated promising results, amon…