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Found 13 papers, 5 papers with code
Learning from Ambiguous Data with Hard Labels
Real-world data often contains intrinsic ambiguity that the common single-hard-label annotation paradigm ignores.
Electrical Load Forecasting Model Using Hybrid LSTM Neural Networks with Online Correction
Accurate electrical load forecasting is of great importance for the efficient operation and control of modern power systems.
Corruptions of Supervised Learning Problems: Typology and Mitigations
We generalize the definition of corruption beyond the concept of distributional shift: corruption includes all modifications of a learning problem, including changes in model class and loss function.
Multi-class Classification from Multiple Unlabeled Datasets with Partial Risk Regularization
Recent years have witnessed a great success of supervised deep learning, where predictive models were trained from a large amount of fully labeled data.
Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients
We propose federation of unsupervised learning (FedUL), where the unlabeled data are transformed into surrogate labeled data for each of the clients, a modified model is trained by supervised FL, and the wanted model is recovered from the modified model.
Rethinking Importance Weighting for Transfer Learning
A key assumption in supervised learning is that training and test data follow the same probability distribution.
Unsupervised Federated Learning is Possible
We propose federation of unsupervised learning (FedUL), where the unlabeled data are transformed into surrogate labeled data for each of the clients, a modified model is trained by supervised FL, and the wanted model is recovered from the modified model.
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification
SSC can be solved by a standard (multi-class) classification method, and we use the SSC solution to obtain the final binary classifier through a certain linear-fractional transformation.
Pointwise Binary Classification with Pairwise Confidence Comparisons
To alleviate the data requirement for training effective binary classifiers in binary classification, many weakly supervised learning settings have been proposed.
A One-step Approach to Covariate Shift Adaptation
A default assumption in many machine learning scenarios is that the training and test samples are drawn from the same probability distribution.
Rethinking Importance Weighting for Deep Learning under Distribution Shift
Under distribution shift (DS) where the training data distribution differs from the test one, a powerful technique is importance weighting (IW) which handles DS in two separate steps: weight estimation (WE) estimates the test-over-training density ratio and weighted classification (WC) trains the classifier from weighted training data.
Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach
The recently proposed unlabeled-unlabeled (UU) classification method allows us to train a binary classifier only from two unlabeled datasets with different class priors.
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data
In this paper, we study training arbitrary (from linear to deep) binary classifier from only unlabeled (U) data by ERM.
