arXiv research

Paper develops methods for semi-supervised Fréchet regression.

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…

In this work, we generalize semi-supervised generative adversarial networks (GANs) from classification problems to regression problems. In the last few years, the importance of improving the training of neural networks using semi-supervised training has been demonstrated for classification problems. We present a novel …

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

Paper tackles RUL prediction with scarce data using indirect supervision.

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

ETM models improve efficiency in semi-supervised logistic regression.

UMAP compared to other methods for dimensionality reduction.

New method shows supervised learning can mimic unsupervised learning effectively.

Semi-supervised learning improves prediction using unlabeled data.

Bayesian Topic Regression models causal inference with text and numerical 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…

Simplifies transfer learning with deep neural networks using ridge regression.

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…

New method estimates covariance in deep heteroscedastic regression without labels.

Unified framework for semi-supervised regression with misspecified models.

Data-dependent hashing has recently attracted attention due to being able to support efficient retrieval and storage of high-dimensional data such as documents, images, and videos. In this paper, we propose a novel learning-based hashing method called "Supervised Discrete Hashing with Relaxation" (SDHR) based on "Super…

We address the problem of general supervised learning when data can only be accessed through an (indefinite) similarity function between data points. Existing work on learning with indefinite kernels has concentrated solely on binary/multi-class classification problems. We propose a model that is generic enough to hand…

Method improves regression models using unlabeled data.

Solves weakly supervised regression using low-rank approximations and manifold regularization.

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

SEMF predicts prediction intervals for ML models using latent variables.

A new framework for predictive clustering and optimization.

This work analyzes the role of data augmentation in self-supervised learning using RKHS approximation and regression.

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

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

We propose the supervised hierarchical Dirichlet process (sHDP), a nonparametric generative model for the joint distribution of a group of observations and a response variable directly associated with that whole group. We compare the sHDP with another leading method for regression on grouped data, the supervised latent…

Study compares non-parametric models for predicting medical insurance reimbursement delays.

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

Study shows how varying levels of supervision and orthonormality constraints affect generalization errors in subspace fitting.

Approach generates multiple correct predictions from single supervision.

Proposes deep quantile regression for uncertainty estimation in lesion detection.

Study compares BERT with other sentiment analysis models.

Enhances supervised learning speed with KT algorithm.

ERFit identifies dynamic equations from data with minimal supervision.

iRDM selects unlabeled samples for regression without labels, improving model accuracy.

Ordinal regression is aimed at predicting an ordinal class label. In this paper, we consider its semi-supervised formulation, in which we have unlabeled data along with ordinal-labeled data to train an ordinal regressor. There are several metrics to evaluate the performance of ordinal regression, such as the mean absol…

UCSL combines clustering with supervised learning to discover interpretable subtypes.

Bayesian SSR on graphs improves regression with noisy labels.

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

Develops a framework for fair semi-supervised learning.

Recently there has been substantial interest in spectral methods for learning dynamical systems. These methods are popular since they often offer a good tradeoff between computational and statistical efficiency. Unfortunately, they can be difficult to use and extend in practice: e.g., they can make it difficult to inco…

The effectiveness of supervised learning techniques has made them ubiquitous in research and practice. In high-dimensional settings, supervised learning commonly relies on dimensionality reduction to improve performance and identify the most important factors in predicting outcomes. However, the economic importance of …

A new learning method using hyperbolic geometry for class labels.

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

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

New approach uses PDE learning for faster RL fine-tuning.