arXiv research

Paper develops methods for semi-supervised Fréchet regression.

Generative adversarial networks improved for regression tasks.

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

Large amounts of labeled data are typically required to train deep learning models. For many real-world problems, however, acquiring additional data can be expensive or even impossible. We present semi-supervised deep kernel learning (SSDKL), a semi-supervised regression model based on minimizing predictive variance in…

Study semi-supervised learning with noisy proxy covariates, deriving bounds and showing gains.

A new framework for semi-supervised ordinal regression.

ETM models improve efficiency in semi-supervised logistic regression.

A method for semi-supervised regression under uncertainty combines graph Laplacian and cluster ensemble.

Unified framework for semi-supervised regression with misspecified models.

This paper presents Correlated Nystrom Views (XNV), a fast semi-supervised algorithm for regression and classification. The algorithm draws on two main ideas. First, it generates two views consisting of computationally inexpensive random features. Second, XNV applies multiview regression using Canonical Correlation Ana…

Develops a semi-supervised learning method using exponential tilt mixture models.

A scalable algorithm improves AUC optimization for semi-supervised ordinal regression.

SDORE uses neural networks to estimate regression functions and their gradients, even with limited labeled data.

Survey explores theoretical limits and gains of semi-supervised learning.

Optimal and safe semi-supervised learning estimator for high-dimensional data.

New method uses unlabeled data to estimate intercept in case-control logistic regression.

Semi-supervised learning improves prediction using unlabeled data.

In this work, we develop a simple algorithm for semi-supervised regression. The key idea is to use the top eigenfunctions of integral operator derived from both labeled and unlabeled examples as the basis functions and learn the prediction function by a simple linear regression. We show that under appropriate assumptio…

Develops a framework for fair semi-supervised learning.

Bayesian analysis shows unlabeled data improve graph-based semi-supervised learning.

New algorithm tackles regression on manifold data using diffusion and semi-supervised learning.

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

Bayesian SSR on graphs improves regression with noisy labels.

A new method improves semi-supervised learning by handling tasks with different attribute spaces.

Study optimal spectral estimator for semi-supervised node classification.

Proposes semi-supervised feature ranking for handling high-dimensional, unlabeled data.

Method improves regression models using unlabeled data.

GNNs improve semi-supervised node regression, but why? We explain.

Prediction-powered causal inference achieves smaller asymptotic variance than traditional methods.

We apply network Lasso to semi-supervised regression problems involving network structured data. This approach lends quite naturally to highly scalable learning algorithms in the form of message passing over an empirical graph which represents the network structure of the data. By using a simple non-parametric regressi…

Proposes an algorithm for semi-supervised learning with budget constraints.

We explore a novel approach to semi-supervised learning. This approach is contrary to the common approach in that the unlabeled examples serve to "muffle," rather than enhance, the guidance provided by the labeled examples. We provide several variants of the basic algorithm and show experimentally that they can achieve…

Enormous online textual information provides intriguing opportunities for understandings of social and economic semantics. In this paper, we propose a novel text regression model based on a conditional generative adversarial network (GAN), with an attempt to associate textual data and social outcomes in a semi-supervis…

Simplifies transfer learning with deep neural networks using ridge regression.

We consider semi-supervised regression when the predictor variables are drawn from an unknown manifold. A simple two step approach to this problem is to: (i) estimate the manifold geodesic distance between any pair of points using both the labeled and unlabeled instances; and (ii) apply a k nearest neighbor regressor b…

Optimal regression with reject option using conditional variance thresholding.

A new method for semi-supervised learning of sparse features using elastic-net.

This article addresses the problem of classification method based on both labeled and unlabeled data, where we assume that a density function for labeled data is different from that for unlabeled data. We propose a semi-supervised logistic regression model for classification problem along with the technique of covariat…

Infinitely wide GCNs perform as GPs for graph semi-supervised learning.

Semi-supervised learning improves QSAR model predictions for novel compounds.

Bayesian optimization improves efficiency with semi-supervised learning.

Active learning improves soft sensor development by suggesting informative labels.

Graph filtering framework improves semi-supervised learning efficiency.

RegMixMatch optimizes Mixup for semi-supervised learning by integrating high- and low-confidence samples.

We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs. The proposed model shows extremely competitive performance when compared to the state-of-the-art graph neural networks on semi-supervised learning benchmark experiments, and outperforms the neural net…

A new framework based on the theory of copulas is proposed to address semi- supervised domain adaptation problems. The presented method factorizes any multivariate density into a product of marginal distributions and bivariate cop- ula functions. Therefore, changes in each of these factors can be detected and corrected…

Propagating input uncertainty through non-linear Gaussian process (GP) mappings is intractable. This hinders the task of training GPs using uncertain and partially observed inputs. In this paper we refer to this task as "semi-described learning". We then introduce a GP framework that solves both, the semi-described and…

We propose a novel sparse preference learning/ranking algorithm. Our algorithm approximates the true utility function by a weighted sum of basis functions using the squared loss on pairs of data points, and is a generalization of the kernel matching pursuit method. It can operate both in a supervised and a semi-supervi…