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Convergence analysis for complementary-label learning with kernel ridge regression
Complementary-label learning (CLL) aims at finding a classifier via samples with complementary labels. Such data is considered to contain less...
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On the grouping effect of the l1−2 models
This paper aims to study the mathematical properties of the l 1−2 models that employ measurement matrices with correlated columns. We first show that...
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Minimizing Robust Estimates of Sums of Parameterized Functions
The author considers the robust approach to constructing machine learning algorithms based on minimizing robust finite sums of parameterized...
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Functionally Logical Modeling of the Σπ-Neuron
In this paper, we suggest a circuitry method of the realization of a logical ΣΠ-neuron. We present digital and hybrid schemes of the logical...
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Peri-Net-Pro: the neural processes with quantified uncertainty for crack patterns
This paper develops a deep learning tool based on neural processes (NPs) called the Peri-Net-Pro, to predict the crack patterns in a moving disk and...
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Differentially private SGD with random features
In the realm of large-scale machine learning, it is crucial to explore methods for reducing computational complexity and memory demands while...
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A station-data-based model residual machine learning method for fine-grained meteorological grid prediction
Fine-grained weather forecasting data, i.e., the grid data with high-resolution, have attracted increasing attention in recent years, especially for...
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Construction of a Logical-Algebraic Corrector to Increase the Adaptive Properties of the ΣΠ-Neuron
In this paper, we consider the problem of constructing a correction algorithm with the aim of increasing the adaptive properties of the ΣΠ-neuron,...
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Learning Rates of Kernel-Based Robust Classification
This paper considers a robust kernel regularized classification algorithm with a non-convex loss function which is proposed to alleviate the...
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Achieving optimal adversarial accuracy for adversarial deep learning using Stackelberg games
The purpose of adversarial deep learning is to train robust DNNs against adversarial attacks, and this is one of the major research focuses of deep...
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An optimal control framework for adaptive neural ODEs
In recent years, the notion of neural ODEs has connected deep learning with the field of ODEs and optimal control. In this setting, neural networks...
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On Reproducing Kernel Banach Spaces: Generic Definitions and Unified Framework of Constructions
Recently, there has been emerging interest in constructing reproducing kernel Banach spaces (RKBS) for applied and theoretical purposes such as...
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Principle of Minimizing Empirical Risk and Averaging Aggregate Functions
In this paper, we propose an extended version of the principle of minimizing empirical risk (ER) based on the use of averaging aggregating functions...
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Learning to select the recombination operator for derivative-free optimization
Extensive studies on selecting recombination operators adaptively, namely, adaptive operator selection (AOS), during the search process of an...
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Pairwise ranking with Gaussian kernel
Regularized pairwise ranking with Gaussian kernels is one of the cutting-edge learning algorithms. Despite a wide range of applications, a rigorous...
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Mathematical Methods of Randomized Machine Learning
In this paper, a review of mathematical methods of randomized machine learning is presented.
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High-resolution signal recovery via generalized sampling and functional principal component analysis
In this paper, we introduce a computational framework for recovering a high-resolution approximation of an unknown function from its low-resolution...
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When is there a representer theorem?
We consider a general regularised interpolation problem for learning a parameter vector from data. The well-known representer theorem says that under...
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Efficient approximation of solutions of parametric linear transport equations by ReLU DNNs
We demonstrate that deep neural networks with the ReLU activation function can efficiently approximate the solutions of various types of parametric...
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Subsampling bias and the best-discrepancy systematic cross validation
Statistical machine learning models should be evaluated and validated before putting to work. Conventional k -fold Monte Carlo cross-validation (MCCV)...
