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Found 20 papers, 9 papers with code
Diffusion models for missing value imputation in tabular data
To our knowledge, less attention has been paid to the investigation of the effectiveness of diffusion models for missing value imputation in tabular data.
On Symmetric Losses for Learning from Corrupted Labels
This paper aims to provide a better understanding of a symmetric loss.
Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification
In contrast to others, our method is model-free and even instance-free.
Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph
To address this challenge, we propose dictionary-based heterogeneous graph neural network (DHGNet) that effectively handles the heterogeneity of DHG by two-step aggregations, which are word-level and language-level aggregations.
Learning from Indirect Observations
Weakly-supervised learning is a paradigm for alleviating the scarcity of labeled data by leveraging lower-quality but larger-scale supervision signals.
Learning from Aggregate Observations
We study the problem of learning from aggregate observations where supervision signals are given to sets of instances instead of individual instances, while the goal is still to predict labels of unseen individuals.
Robust Imitation Learning from Noisy Demonstrations
Robust learning from noisy demonstrations is a practical but highly challenging problem in imitation learning.
Unsupervised Domain Adaptation Based on Source-guided Discrepancy
A previously proposed discrepancy that does not use the source domain labels requires high computational cost to estimate and may lead to a loose generalization error bound in the target domain.
Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error
Based on the analysis of the Bayes optimal classifier, we show that given a test class prior, PU classification under class prior shift is equivalent to PU classification with asymmetric error.
Imitation Learning from Imperfect Demonstration
Imitation learning (IL) aims to learn an optimal policy from demonstrations.
On the Calibration of Multiclass Classification with Rejection
First, we consider an approach based on simultaneous training of a classifier and a rejector, which achieves the state-of-the-art performance in the binary case.
Domain Discrepancy Measure for Complex Models in Unsupervised Domain Adaptation
Appropriately evaluating the discrepancy between domains is essential for the success of unsupervised domain adaptation.
Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization
We further provide an estimation error bound to show that our risk estimator is consistent.
Classification from Triplet Comparison Data
Although learning from triplet comparison data has been considered in many applications, an important fundamental question of whether we can learn a classifier only from triplet comparison data has remained unanswered.
Learning Only from Relevant Keywords and Unlabeled Documents
We consider a document classification problem where document labels are absent but only relevant keywords of a target class and unlabeled documents are given.
Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time
If the black-box function varies with time, then time-varying Bayesian optimization is a promising framework.
Classification with Rejection Based on Cost-sensitive Classification
The goal of classification with rejection is to avoid risky misclassification in error-critical applications such as medical diagnosis and product inspection.
On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective
In this paper, we first prove that the focal loss is classification-calibrated, i. e., its minimizer surely yields the Bayes-optimal classifier and thus the use of the focal loss in classification can be theoretically justified.
A Symmetric Loss Perspective of Reliable Machine Learning
When minimizing the empirical risk in binary classification, it is a common practice to replace the zero-one loss with a surrogate loss to make the learning objective feasible to optimize.
Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics
In this paper, we propose Virtual Human Generative Model (VHGM), a machine learning model for estimating attributes about healthcare, lifestyles, and personalities.
