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Found 18 papers, 7 papers with code
A stochastic optimization approach to train non-linear neural networks with a higher-order variation regularization
While the $(k, q)$-VR terms applied to general parametric models are computationally intractable due to the integration, this study provides a stochastic optimization algorithm, that can efficiently train general models with the $(k, q)$-VR without conducting explicit numerical integration.
Minimizing robust density power-based divergences for general parametric density models
Density power divergence (DPD) is designed to robustly estimate the underlying distribution of observations, in the presence of outliers.
Autoregressive with Slack Time Series Model for Forecasting a Partially-Observed Dynamical Time Series
This study delves into the domain of dynamical systems, specifically the forecasting of dynamical time series defined through an evolution function.
An interpretable neural network-based non-proportional odds model for ordinal regression
This study proposes an interpretable neural network-based non-proportional odds model (N$^3$POM) for ordinal regression.
A Greedy and Optimistic Approach to Clustering with a Specified Uncertainty of Covariates
In this study, we examine a clustering problem in which the covariates of each individual element in a dataset are associated with an uncertainty specific to that element.
Improving Nonparametric Classification via Local Radial Regression with an Application to Stock Prediction
For correcting the asymptotic bias with fewer observations, this paper proposes a \emph{local radial regression (LRR)} and its logistic regression variant called \emph{local radial logistic regression~(LRLR)}, by combining the advantages of LPoR and MS-$k$-NN.
A generalization gap estimation for overparameterized models via the Langevin functional variance
This paper discusses the estimation of the generalization gap, the difference between generalization performance and training performance, for overparameterized models including neural networks.
Minimax Analysis for Inverse Risk in Nonparametric Planer Invertible Regression
The derived minimax rate corresponds to that of the non-invertible bi-Lipschitz function, which shows that the invertibility does not reduce the complexity of the estimation problem in terms of the rate.
Dependence of variance on covariate design in nonparametric link regression
This paper discusses a design-dependent nature of variance in nonparametric link regression aiming at predicting a mean outcome at a link, i. e., a pair of nodes, based on currently observed data comprising covariates at nodes and outcomes at links.
Statistics Theory Statistics Theory
Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate
The weights and the parameter $k \in \mathbb{N}$ regulate its bias-variance trade-off, and the trade-off implicitly affects the convergence rate of the excess risk for the $k$-NN classifier; several existing studies considered selecting optimal $k$ and weights to obtain faster convergence rate.
Stochastic Neighbor Embedding of Multimodal Relational Data for Image-Text Simultaneous Visualization
Multimodal relational data analysis has become of increasing importance in recent years, for exploring across different domains of data, such as images and their text tags obtained from social networking services (e. g., Flickr).
Extrapolation Towards Imaginary $0$-Nearest Neighbour and Its Improved Convergence Rate
The weights and the parameter $k \in \mathbb{N}$ regulate its bias-variance trade-off, and the trade-off implicitly affects the convergence rate of the excess risk for the $k$-NN classifier; several existing studies considered selecting optimal $k$ and weights to obtain faster convergence rate.
Hyperlink Regression via Bregman Divergence
A collection of $U \: (\in \mathbb{N})$ data vectors is called a $U$-tuple, and the association strength among the vectors of a tuple is termed as the \emph{hyperlink weight}, that is assumed to be symmetric with respect to permutation of the entries in the index.
Representation Learning with Weighted Inner Product for Universal Approximation of General Similarities
In addition to the parameters of neural networks, we optimize the weights of the inner product by allowing positive and negative values.
Robust Graph Embedding with Noisy Link Weights
We propose $\beta$-graph embedding for robustly learning feature vectors from data vectors and noisy link weights.
Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability
We propose shifted inner-product similarity (SIPS), which is a novel yet very simple extension of the ordinary inner-product similarity (IPS) for neural-network based graph embedding (GE).
On representation power of neural network-based graph embedding and beyond
We consider the representation power of siamese-style similarity functions used in neural network-based graph embedding.
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks
PMvGE is a probabilistic model for predicting new associations via graph embedding of the nodes of data vectors with links of their associations.
