arXiv research

BHLR predicts hyperlink weights from data vectors using symmetric similarity functions and Bregman divergence.

Proposes a novel tensor-based approach for multi-level link prediction.

A hyperlink is a finite set of non-intersecting simple closed curves in $\mathbb{R} \times \mathbb{R}^3$. We compute the Wilson Loop observable using a path integral with an Einstein-Hilbert action. Using axial-gauge fixing, we can write this path integral as the limit of a sequence of Chern-Simons integrals, studied e…

A novel multilayer network approach for text analysis.

A hyperlink is a finite set of non-intersecting simple closed curves in $\mathbb{R} \times \mathbb{R}^3$. Let $S$ be an orientable surface in $\mathbb{R}^3$. The dynamical variables in General Relativity are the vierbein $e$ and a $\mathfrak{su}(2)\times\mathfrak{su}(2)$-valued connection $ω$. Together with Minkowski m…

A training-free message passing module improves hypergraph neural networks.

Wiki-CS dataset benchmarks Graph Neural Networks using Wikipedia articles.

We present the mixture-of-parents maximum entropy Markov model (MoP-MEMM), a class of directed graphical models extending MEMMs. The MoP-MEMM allows tractable incorporation of long-range dependencies between nodes by restricting the conditional distribution of each node to be a mixture of distributions given the parent…

In this paper we propose a Bayesian nonparametric approach to modelling sparse time-varying networks. A positive parameter is associated to each node of a network, which models the sociability of that node. Sociabilities are assumed to evolve over time, and are modelled via a dynamic point process model. The model is a…

Many classification problems involve data instances that are interlinked with each other, such as webpages connected by hyperlinks. Techniques for "collective classification" (CC) often increase accuracy for such data graphs, but usually require a fully-labeled training graph. In contrast, we examine how to improve the…

New method tests causal association using noise contrastive backdoor adjustment.

Infrastructure monitors AI/ML radiology models across multiple sites.

Many data sets contain rich information about objects, as well as pairwise relations between them. For instance, in networks of websites, scientific papers, and other documents, each node has content consisting of a collection of words, as well as hyperlinks or citations to other nodes. In order to perform inference on…

LinkedRNN models linked sequences by capturing both sequential and link information.

Max-MIG tackles crowdsourced label learning without knowing crowd information structure.

Ability for accurate hospital case cost modelling and prediction is critical for efficient health care financial management and budgetary planning. A variety of regression machine learning algorithms are known to be effective for health care cost predictions. The purpose of this experiment was to build an Azure Machine…

Efficiently private regression for unbounded data.

This paper studies robust regression in the settings of Huber's $ε$-contamination models. We consider estimators that are maximizers of multivariate regression depth functions. These estimators are shown to achieve minimax rates in the settings of $ε$-contamination models for various regression problems including nonpa…

This paper studies the nonparametric modal regression problem systematically from a statistical learning view. Originally motivated by pursuing a theoretical understanding of the maximum correntropy criterion based regression (MCCR), our study reveals that MCCR with a tending-to-zero scale parameter is essentially moda…

Proposes FARM model combining latent factor and sparse regression.

Neural regression trees convert regression to classification more effectively.

Survey of SDR methods for high-dimensional regression and embedding.

This paper reviews SDR methods for multivariate response regression.

Paper introduces semi-supervised linear extremile regression for high-dimensional data.

Improves logistic regression performance with nonconvex programming.

Study improves $H$-consistency bounds for regression analysis.

Prevalidated ridge regression simplifies logistic regression for high-dimensional data.

Introduces a new model for mapping matrices to matrices, subsuming linear regression.

Meta-theorems validate fair regression algorithms under demographic parity constraints.

We analyzed optimism in linear and kernel regression models.

A new optimizer, MVO, improves nonlinear regression performance.

Brenier isotonic regression extends multi-output isotonic regression using optimal transport.

We simplify complex regression coefficients using linearization and feature comparison.

Unified framework for fair regression under demographic parity.

Huber regression assessed for robustness in statistical learning.

Locally adaptive interpretable regression improves linear regression's predictability.

The paper improves SVR with linear constraints for better model properties.

We analyze coresets for regularized regression problems and propose a modified lasso that yields smaller coresets.

Least Angle Regression is a promising technique for variable selection applications, offering a nice alternative to stepwise regression. It provides an explanation for the similar behavior of LASSO ($\ell_1$-penalized regression) and forward stagewise regression, and provides a fast implementation of both. The idea has…

DualIV simplifies non-linear IV regression via dual formulation.

Study uniform consistency in nonparametric mixture models and mixed regression.

We find a convex model for traditional nonlinear regression under L2 loss.

Collider regression improves predictive performance in regression tasks.

New GP model estimates piecewise continuous functions.

PARC uses piecewise linear predictors for regression and classification.

We describe dimensionally constrained symbolic regression which has been developed for mass measurement in certain classes of events in high-energy physics (HEP). With symbolic regression, we can derive equations that are well known in HEP. However, in problems with large number of variables, we find that by constraini…

This work analyzes Fréchet regression using comparison geometry, providing theoretical and practical insights.

Study on reducing dimensionality in high-dimensional regression with kernel methods and stability analysis.