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06/12/2023
Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction
The mean-field Langevin dynamics (MFLD) is a nonlinear generalization of...
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05/13/2023
Tight and fast generalization error bound of graph embedding in metric space
Recent studies have experimentally shown that we can achieve in non-Eucl...
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03/06/2023
Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems
The entropic fictitious play (EFP) is a recently proposed algorithm that...
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02/18/2023
Parameter Averaging for SGD Stabilizes the Implicit Bias towards Flat Regions
Stochastic gradient descent is a workhorse for training deep neural netw...
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02/12/2023
Koopman-Based Bound for Generalization: New Aspect of Neural Networks Regarding Nonlinear Noise Filtering
We propose a new bound for generalization of neural networks using Koopm...
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01/25/2022
Convex Analysis of the Mean Field Langevin Dynamics
As an example of the nonlinear Fokker-Planck equation, the mean field La...
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05/21/2021
Generalization Error Bound for Hyperbolic Ordinal Embedding
Hyperbolic ordinal embedding (HOE) represents entities as points in hype...
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03/11/2021
BODAME: Bilevel Optimization for Defense Against Model Extraction
Model extraction attacks have become serious issues for service provider...
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12/31/2020
Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis
We propose the particle dual averaging (PDA) method, which generalizes t...
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07/31/2020
A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification
Spatial attention has been introduced to convolutional neural networks (...
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07/23/2020
Online Robust and Adaptive Learning from Data Streams
In online learning from non-stationary data streams, it is both necessar...
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06/22/2020
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime
We analyze the convergence of the averaged stochastic gradient descent f...
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06/18/2020
When Does Preconditioning Help or Hurt Generalization?
While second order optimizers such as natural gradient descent (NGD) oft...
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11/13/2019
Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features
Although kernel methods are widely used in many learning problems, they ...
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10/28/2019
Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space
Deep learning has exhibited superior performance for various tasks, espe...
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06/20/2019
Data Cleansing for Models Trained with SGD
Data cleansing is a typical approach used to improve the accuracy of mac...
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05/23/2019
Refined Generalization Analysis of Gradient Descent for Over-parameterized Two-layer Neural Networks with Smooth Activations on Classification Problems
Recently, several studies have proven the global convergence and general...
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06/14/2018
Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors
We consider stochastic gradient descent for binary classification proble...
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02/25/2018
Functional Gradient Boosting based on Residual Network Perception
Residual Networks (ResNets) have become state-of-the-art models in deep ...
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01/07/2018
Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models
We propose a new technique that boosts the convergence of training gener...
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12/14/2017
Stochastic Particle Gradient Descent for Infinite Ensembles
The superior performance of ensemble methods with infinite models are we...
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06/09/2015