Improves anomaly detection with contaminated unlabeled data.
problem Weakness in existing semi-supervised anomaly detection methods when unlabeled data contain anomalies.
method Integrates positive-unlabeled learning with deep anomaly detection models.
result Achieves better detection performance on various datasets.
Prevents sensitive data generation in diffusion models using labeled and unlabeled data.
problem Generating sensitive data in diffusion models using unlabeled data.
method Positive-Unlabeled Diffusion Models, approximating ELBO with labeled and unlabeled data.
result Prevents the generation of sensitive data without compromising image quality.
Paper tackles leveraging unlabeled data for PU classification and robust generation.
problem Scarcity of labeled data in machine learning problems.
method Introduces a novel training framework that simultaneously targets PU classification and conditional generation using extra unlabeled data.
result Proves the effectiveness of a Classifier-Noise-Invariant Conditional GAN (CNI-CGAN) that enhances PU classifier performance and leverages extra data.
New methods learn from PU data with non-representative positives.
problem Learning from PU data with non-representative positive classes.
method Integrates negative-unlabeled and unlabeled-unlabeled learning, or uses a recursive risk estimator.
result Effective across various real-world datasets and forms of positive bias.
ORIL learns a reward function from unlabeled data to improve robot learning.
problem Leveraging unlabeled data for robot learning.
method ORIL learns a reward function from demonstrator and unlabeled trajectories, annotates data, and trains an agent via offline reinforcement learning.
result ORIL consistently outperforms BC agents on various robotic tasks.
Enhances adversarial robustness with unlabeled out-of-domain data.
problem Improving robustness of models against adversarial attacks.
method Leveraging unlabeled data from multiple domains to bridge the sample complexity gap in adversarial robustness.
result Better adversarial robustness achieved when unlabeled data comes from a shifted domain.
The article explains how to estimate confusion matrices for classifiers using unlabeled data.
problem Estimating sensitivity and specificity of binary medical diagnostic tests without gold standard tests.
method Modifying diagnostic test solutions to estimate confusion matrices for classifiers on unlabeled data.
result The approach can be used to estimate accuracy statistics for supervised or unsupervised binary classifiers on unlabeled data.
New method learns to weight unlabeled data in semi-supervised learning.
problem Equal weighting of all unlabeled data in semi-supervised learning.
method Adjust weights for each unlabeled example using influence function.
result Technique outperforms state-of-the-art methods on image and language classification tasks.
Bayesian framework uses unlabeled data to improve fairness assessment.
problem Reliable fairness assessment with limited labeled data.
method Hierarchical latent variable model with Bayesian inference.
result Significant reduction in estimation error for fairness metrics.
Paper analyzes how unlabeled data improves SSL and adversarial robustness.
problem Understanding how unlabeled data impacts SSL and adversarial robustness.
method Minimax analysis and adversarial training.
result Reconstruction-based SSL algorithm is rate-optimal under various models and enhances adversarial robustness.
New research shows unlabeled data is equally valuable as labeled data in certain semi-supervised learning scenarios.
problem Improving learning performance with limited labeled data.
method Statistical models with continuous parameters, showing equal utility of unlabeled data under specific conditions.
result The learning rate of semi-supervised learning scales similarly to supervised learning when unlabeled data is abundant.
LoD improves model safety by integrating unlabeled wild data, reducing OOD misclassification.
problem Improving model safety and reliability using unlabeled wild data containing both in-distribution and out-of-distribution samples.
method Intentionally label-noisifying unlabeled wild data to enable joint learning of labeled ID and OOD data, distinguishing losses between ID and OOD samples.
result LoD framework achieves superior OOD detection without requiring thresholds, improving model safety.
New method uses unlabelled data to improve Bayesian Neural Networks.
problem Lack of ability to use unlabelled data in conventional Bayesian Neural Networks.
method Self-supervised Bayesian Neural Networks using contrastive pretraining and variational lower bound optimization.
result Prior predictive distributions capture problem semantics better and improve predictive performance.
Surveying how to use unlabeled data in federated learning.
problem Costly labeling of data limits FL applications.
method Survey and analyze existing research.
result Potential for using unlabeled data in FL.
The paper proposes a SSL framework for complex causal models using unlabelled data.
problem Understanding how unlabelled data can improve SSL in complex causal models.
method The paper explores flexible causal graph structures and designs causal generative models to generate synthetic labelled data.
result The proposed method effectively improves predictive model accuracy using synthetic labelled data generated from unlabelled data.
ORDisCo learns from unlabeled data to improve semi-supervised continual learning.
problem Lack of effective use of unlabeled data in semi-supervised continual learning.
method Deep Online Replay with Discriminator Consistency (ORDisCo) that continually passes the learned data distribution to a classifier and selectively stabilizes discriminator parameters.
result Significant performance improvement on various semi-supervised learning benchmark datasets.
Paper tackles survival data analysis with positive and unlabeled observations.
problem Traditional survival analysis yields biased results with positive-unlabeled data.
method Developed parametric, nonparametric, and machine learning models for positive and unlabeled survival data.
result Proposed estimation method provides valid results for positive-unlabeled survival data.
Improving a semi-supervised image segmentation task has the option of adding more unlabelled images, labelling the unlabelled images or combining both, as neither image acquisition nor expert labelling can be considered trivial in most clinical applications. With a laparoscopic liver image segmentation application, we …
New framework assesses value of labeled vs unlabeled data in latent variable models.
problem Determining the optimal use of labeled and unlabeled data in latent variable models.
method Developed a bias-variance decomposition of the generalization error for method-of-moments latent variable estimation, and introduced a correction for misspecification.
result Labeled data is more valuable than unlabeled data when models are misspecified, but this value can be reduced with correction.
Deep RL detects anomalies from few labeled examples and large unlabeled data.
problem Anomaly detection with limited labeled data and large unlabeled data.
method Deep reinforcement learning to optimize detection of labeled and unlabeled anomalies.
result Significantly outperforms state-of-the-art methods on 48 real-world datasets.
Bayesian analysis shows unlabeled data improve graph-based semi-supervised learning.
problem Improving semi-supervised learning with limited labeled data.
method Bayesian nonparametric approach using unlabeled data for graph-based learning.
result Posterior contracts optimally around the truth with sufficient unlabeled data.
New STKR estimators use unlabeled data for smoother function learning.
problem Leveraging unlabeled data for smoother function learning.
method Spectrally transformed kernel regression (STKR) with scalable implementations.
result STKR can learn any sufficiently smooth function.
SAL framework uses unlabeled data to improve OOD detection.
problem Lack of clean OOD samples makes OOD detection challenging.
method SAL framework separates candidate outliers and trains an OOD classifier.
result SAL achieves state-of-the-art performance on benchmarks.
We quantify the separation between the numbers of labeled examples required to learn in two settings: Settings with and without the knowledge of the distribution of the unlabeled data. More specifically, we prove a separation by Θ(logn) multiplicative factor for the class of projections over the Boolean hypercube o…
Method improves regression models using unlabeled data.
problem Improving predictive performance of regression models with limited labeled data.
method Mixed semi-supervised generalized-linear-regression with different mixing mechanisms.
result Integrating unlabeled data consistently improves predictive performance.
Compressing giant neural networks has gained much attention for their extensive applications on edge devices such as cellphones. During the compressing process, one of the most important procedures is to retrain the pre-trained models using the original training dataset. However, due to the consideration of security, p…
Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literatur…
Deep neural networks (DNNs) are incredibly brittle due to adversarial examples. To robustify DNNs, adversarial training was proposed, which requires large-scale but well-labeled data. However, it is quite expensive to annotate large-scale data well. To compensate for this shortage, several seminal works are utilizing l…
TAP transfers knowledge from unlabeled data to improve cross-modal learning.
problem Improving supervised learning performance using unlabeled data from a different modality.
method Probabilistic approach for missing information estimation, kernel regression, cross-attention module, TAP neural network.
result TAP significantly improves generalization across different domains and neural network architectures.
Assessing the performance of a learned model is a crucial part of machine learning. However, in some domains only positive and unlabeled examples are available, which prohibits the use of most standard evaluation metrics. We propose an approach to estimate any metric based on contingency tables, including ROC and PR cu…
Dash selects dynamic pseudo labels from unlabeled data for semi-supervised learning.
problem Efficiently using unlabeled data in semi-supervised learning while avoiding incorrect pseudo labels.
method Dynamic thresholding to select a subset of unlabeled examples for training.
result Dash achieves theoretical convergence and outperforms state-of-the-art methods empirically.
GPNs use unlabeled data to estimate uncertainty in Bayesian problems.
problem Limited training data in high-dimensional problems.
method Generative Posterior Networks (GPNs) that approximate the posterior distribution using unlabeled data.
result GPNs improve epistemic uncertainty estimation and scalability.
Improves PU learning for imbalanced data with practical AUL estimation and new training method.
problem Training binary classifiers on datasets with only positive and unlabeled samples.
method Asymptotic unbiased AUL estimation and ProbTagging for imbalanced data.
result ProbTagging increases AUC by up to 10% on industrial and artificial data sets.
MixMOOD improves SSDL by selecting unlabelled data based on deep feature similarity.
problem Class distribution mismatch in semi-supervised learning.
method MixMOOD uses deep dataset dissimilarity measures to select unlabelled data.
result MixMOOD selects unlabelled data based on strong correlation with MixMatch accuracy.
The paper resolves the paradox of using unlabeled data for treatment effect estimation.
problem Using unlabeled data to estimate propensity scores for treatment effect estimation.
method Proposes a simple procedure to reconcile the use of estimated propensity scores with the advice to use true propensity scores.
result Direct regression may be preferable to inverse-propensity weighting in many circumstances.
This paper defines a positive and unlabeled classification problem for standard GANs, which then leads to a novel technique to stabilize the training of the discriminator in GANs. Traditionally, real data are taken as positive while generated data are negative. This positive-negative classification criterion was kept f…
Paper improves robustness of ABI with unlabeled data.
problem Robustness issues in ABI for out-of-simulation data.
method Semi-supervised approach using unlabeled data and self-consistency losses.
result Semi-supervised ABI with unlabeled data improves robustness significantly.
Better use of unlabelled data improves Bayesian active learning models.
problem Neglecting unlabelled data harms predictive performance and data acquisition decisions.
method A simple framework for semi-supervised Bayesian active learning.
result The proposed framework produces better models than conventional approaches.
New research shows semantic data matching can degrade SSDL performance.
problem The limits of semantic data set matching in semi-supervised learning.
method Demonstrated through simulations and a new dissimilarity measure.
result Semantic data matching can degrade SSDL performance under non-IID data.
The study explores using unlabeled data to improve survival time predictions.
problem Challenges in clinical follow-up studies due to drop-out and data collection issues.
method Investigates three approaches to incorporate unlabeled data in survival analysis.
result All approaches improve predictive performance over independent test data.
A new method predicts true classes from positive and unlabeled data with additional labeled observations.
problem Predicting true classes from positive and unlabeled data with selection bias.
method Introduces augmented PU prediction, allowing feature-dependent labeling, and compares various empirical Bayes rules.
result The variational autoencoder-based method performs similarly or better than other methods and improves accuracy for unlabeled samples.
Medix uses the median to detect outliers from unlabeled data for robust OOD detection.
problem Challenges in using unlabeled data for OOD detection due to mixed InD and OOD samples.
method Introduces Medix, a framework using the median operation to identify outliers from unlabeled data.
result Empirical results show Medix outperforms existing methods in open-world settings.
Improves SSL with doubly robust estimation of unlabeled class distribution.
problem Limited labeled data and long-tailed class distributions in unlabeled data.
method Explicitly estimate unlabeled class distribution using doubly robust estimator.
result Improves performance of SSL methods on unlabeled data.
In semi-supervised classification, one is given access both to labeled and unlabeled data. As unlabeled data is typically cheaper to acquire than labeled data, this setup becomes advantageous as soon as one can exploit the unlabeled data in order to produce a better classifier than with labeled data alone. However, the…
Survey on self-supervised pre-training for neural networks using unlabeled data.
problem Improving model performance using unlabeled data.
method Pre-training on unlabeled data followed by task-specific adaptation.
result Enhanced model performance through self-supervised pre-training.
Generative model creates synthetic unlabeled data for SSL.
problem Training SSL models without real unlabeled datasets.
method Meta-optimized synthetic samples generated from generative models.
result Synthetic samples improve SSL performance more efficiently than real unlabeled data.
Boosting with unlabeled data achieves optimal sample complexity in agnostic settings.
problem Boosting's sample inefficiency in agnostic learning.
method Designing an agnostic boosting algorithm with unlabeled data to match ERM's sample complexity.
result The total sample complexity is optimal, with a vanishing fraction needing to be labeled.
Semi-supervised learning improves classification in high dimensions.
problem Combining labeled and unlabeled data for high-dimensional classification.
method Information theoretic and computational lower bounds analysis for feature selection.
result Semi-supervised learning is advantageous for classification in high dimensions.