research
∙
06/08/2023
Federated Learning under Covariate Shifts with Generalization Guarantees
This paper addresses intra-client and inter-client covariate shifts in f...
share
research
∙
05/30/2023
Benign Overfitting in Deep Neural Networks under Lazy Training
This paper focuses on over-parameterized deep neural networks (DNNs) wit...
research
∙
04/25/2023
Provable benefits of general coverage conditions in efficient online RL with function approximation
In online reinforcement learning (RL), instead of employing standard str...
research
∙
09/18/2022
Random Fourier Features for Asymmetric Kernels
The random Fourier features (RFFs) method is a powerful and popular tech...
research
∙
09/16/2022
Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study
Neural tangent kernel (NTK) is a powerful tool to analyze training dynam...
research
∙
09/15/2022
Understanding Deep Neural Function Approximation in Reinforcement Learning via ε-Greedy Exploration
This paper provides a theoretical study of deep neural function approxim...
research
∙
09/15/2022
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)
We study the average robustness notion in deep neural networks in (selec...
research
∙
09/15/2022
Generalization Properties of NAS under Activation and Skip Connection Search
Neural Architecture Search (NAS) has fostered the automatic discovery of...
research
∙
09/15/2022
Sound and Complete Verification of Polynomial Networks
Polynomial Networks (PNs) have demonstrated promising performance on fac...
research
∙
10/13/2021
On the Double Descent of Random Features Models Trained with SGD
We study generalization properties of random features (RF) regression in...
research
∙
11/03/2020
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines
In this paper, we develop a quadrature framework for large-scale kernel ...
research
∙
10/06/2020
Kernel regression in high dimension: Refined analysis beyond double descent
In this paper, we provide a precise characterize of generalization prope...
research
∙
09/10/2020
End-to-end Kernel Learning via Generative Random Fourier Features
Random Fourier features enable researchers to build feature map to learn...
research
∙
06/01/2020
Analysis of Least Squares Regularized Regression in Reproducing Kernel Krein Spaces
In this paper, we study the asymptotical properties of least squares reg...
research
∙
05/30/2020
Generalizing Random Fourier Features via Generalized Measures
We generalize random Fourier features, that usually require kernel funct...
research
∙
04/23/2020
Random Features for Kernel Approximation: A Survey in Algorithms, Theory, and Beyond
Random features is one of the most sought-after research topics in stati...
research
∙
11/20/2019
Random Fourier Features via Fast Surrogate Leverage Weighted Sampling
In this paper, we propose a fast surrogate leverage weighted sampling st...
research
∙
10/07/2019
Deep Kernel Learning via Random Fourier Features
Kernel learning methods are among the most effective learning methods an...
research
∙
04/15/2019
Robust Visual Tracking Revisited: From Correlation Filter to Template Matching
In this paper, we propose a novel matching based tracker by investigatin...
research
∙
09/26/2018
Generalization Properties of hyper-RKHS and its Application to Out-of-Sample Extensions
Hyper-kernels endowed by hyper-Reproducing Kernel Hilbert Space (hyper-R...
research
∙
08/31/2018
Learning Data-adaptive Nonparametric Kernels
Traditional kernels or their combinations are often not sufficiently fle...
research
∙
07/06/2017
Indefinite Kernel Logistic Regression
Traditionally, kernel learning methods requires positive definitiveness ...
research
∙
10/05/2015
