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Found 11 papers, 2 papers with code
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning
In PU learning, a binary classifier is trained from positive (P) and unlabeled (U) data without negative (N) data.
Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data
Most of the semi-supervised classification methods developed so far use unlabeled data for regularization purposes under particular distributional assumptions such as the cluster assumption.
Convex Formulation of Multiple Instance Learning from Positive and Unlabeled Bags
Multiple instance learning (MIL) is a variation of traditional supervised learning problems where data (referred to as bags) are composed of sub-elements (referred to as instances) and only bag labels are available.
Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning
Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced classification.
Information-Theoretic Representation Learning for Positive-Unlabeled Classification
Recent advances in weakly supervised classification allow us to train a classifier only from positive and unlabeled (PU) data.
Binary Matrix Completion Using Unobserved Entries
In this paper, motivated by a semi-supervised classification method recently proposed by Sakai et al. (2017), we develop a method for the BMC problem which can use all of positive, negative, and unobserved entries, by combining the risks of Davenport et al. (2014) and Hsieh et al. (2015).
Robust modal regression with direct log-density derivative estimation
In order to apply a gradient method for the maximization, the fundamental challenge is accurate approximation of the gradient of MRR, not MRR itself.
Do We Need Zero Training Loss After Achieving Zero Training Error?
We experimentally show that flooding improves performance and, as a byproduct, induces a double descent curve of the test loss.
Regret Minimization for Causal Inference on Large Treatment Space
Predicting which action (treatment) will lead to a better outcome is a central task in decision support systems.
Predictive Optimization with Zero-Shot Domain Adaptation
The task is regarded as predictive optimization, but existing predictive optimization methods have not been extended to handling multiple domains.
Unsupervised Deep Learning by Injecting Low-Rank and Sparse Priors
What if deep neural networks can learn from sparsity-inducing priors?
