
Estimation error analysis of deep learning on the regression problem on the variable exponent Besov space
Deep learning has achieved notable success in various fields, including ...
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Neural Architecture Search Using Stable Rank of Convolutional Layers
In Neural Architecture Search (NAS), Differentiable ARchiTecture Search ...
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Generalization bound of globally optimal nonconvex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics
We introduce a new theoretical framework to analyze deep learning optimi...
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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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Gradient Descent in RKHS with Importance Labeling
Labeling cost is often expensive and is a fundamental limitation of supe...
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When Does Preconditioning Help or Hurt Generalization?
While second order optimizers such as natural gradient descent (NGD) oft...
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Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multiscale Graph Neural Networks
It is known that the current graph neural networks (GNNs) are difficult ...
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Selective Inference for Latent Block Models
Model selection in latent block models has been a challenging but import...
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Meta Cyclical Annealing Schedule: A Simple Approach to Avoiding MetaAmortization Error
The ability to learn new concepts with small amounts of data is a crucia...
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Dimensionfree convergence rates for gradient Langevin dynamics in RKHS
Gradient Langevin dynamics (GLD) and stochastic GLD (SGLD) have attracte...
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Understanding Generalization in Deep Learning via Tensor Methods
Deep neural networks generalize well on unseen data though the number of...
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Domain Adaptation Regularization for Spectral Pruning
Deep Neural Networks (DNNs) have recently been achieving stateofthear...
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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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Scalable Deep Neural Networks via LowRank Matrix Factorization
Compressing deep neural networks (DNNs) is important for realworld appl...
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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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Compression based bound for noncompressed network: unified generalization error analysis of large compressible deep neural network
One of biggest issues in deep learning theory is its generalization abil...
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Understanding the Effects of PreTraining for Object Detectors via Eigenspectrum
ImageNet pretraining has been regarded as essential for training accura...
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Gradient Noise Convolution (GNC): Smoothing Loss Function for Distributed LargeBatch SGD
Largebatch stochastic gradient descent (SGD) is widely used for trainin...
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Goodnessoffit Test for Latent Block Models
Latent Block Models are used for probabilistic biclustering, which is sh...
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Accelerated Sparsified SGD with Error Feedback
We study a stochastic gradient method for synchronous distributed optimi...
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On Asymptotic Behaviors of Graph CNNs from Dynamical Systems Perspective
Graph Convolutional Neural Networks (graph CNNs) are a promising deep le...
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Refined Generalization Analysis of Gradient Descent for Overparameterized Twolayer Neural Networks with Smooth Activations on Classification Problems
Recently, several studies have proven the global convergence and general...
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On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces
Deep learning has been applied to various tasks in the field of machine ...
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Approximation and Nonparametric Estimation of ResNettype Convolutional Neural Networks
Convolutional neural networks (CNNs) have been shown to achieve optimal ...
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Adam Induces Implicit Weight Sparsity in Rectifier Neural Networks
In recent years, deep neural networks (DNNs) have been applied to variou...
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Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality
Deep learning has shown high performances in various types of tasks from...
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Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation
We develop new stochastic gradient methods for efficiently solving spars...
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SpectralPruning: Compressing deep neural network via spectral analysis
The model size of deep neural network is getting larger and larger to re...
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Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors
We consider stochastic gradient descent for binary classification proble...
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Crossdomain Recommendation via Deep Domain Adaptation
The behavior of users in certain services could be a clue that can be us...
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Functional Gradient Boosting based on Residual Network Perception
Residual Networks (ResNets) have become stateoftheart models in deep ...
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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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Stochastic Particle Gradient Descent for Infinite Ensembles
The superior performance of ensemble methods with infinite models are we...
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Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables
Sparse regularization such as ℓ_1 regularization is a quite powerful and...
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Fast learning rate of deep learning via a kernel perspective
We develop a new theoretical framework to analyze the generalization err...
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Trimmed Density Ratio Estimation
Density ratio estimation is a vital tool in both machine learning and st...
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Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization
In this paper, we develop a new accelerated stochastic gradient method f...
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Learning Sparse Structural Changes in Highdimensional Markov Networks: A Review on Methodologies and Theories
Recent years have seen an increasing popularity of learning the sparse c...
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Stochastic dual averaging methods using variance reduction techniques for regularized empirical risk minimization problems
We consider a composite convex minimization problem associated with regu...
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Structure Learning of Partitioned Markov Networks
We learn the structure of a Markov Network between two groups of random ...
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Spectral norm of random tensors
We show that the spectral norm of a random n_1× n_2×...× n_K tensor (or ...
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Support Consistency of Direct SparseChange Learning in Markov Networks
We study the problem of learning sparse structure changes between two Ma...
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Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
We propose a new method for detecting changes in Markov network structur...
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Convex Tensor Decomposition via Structured Schatten Norm Regularization
We discuss structured Schatten norms for tensor decomposition that inclu...
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DensityDifference Estimation
We address the problem of estimating the difference between two probabil...
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A Conjugate Property between Loss Functions and Uncertainty Sets in Classification Problems
In binary classification problems, mainly two approaches have been propo...
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Fast learning rate of multiple kernel learning: Tradeoff between sparsity and smoothness
We investigate the learning rate of multiple kernel learning (MKL) with ...
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Fast Learning Rate of NonSparse Multiple Kernel Learning and Optimal Regularization Strategies
In this paper, we give a new generalization error bound of Multiple Kern...
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Relative DensityRatio Estimation for Robust Distribution Comparison
Divergence estimators based on direct approximation of densityratios wi...
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Fast Learning Rate of lpMKL and its Minimax Optimality
In this paper, we give a new sharp generalization bound of lpMKL which ...
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Taiji Suzuki
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Associate Professor in Department of Mathematical Informatics and Graduate School of Information Science and Technology at University of Tokyo, Center for Advanced Integrated Intelligence Research, RIKEN, Tokyo