The paper proposes a tensor‑optimization‑powered ensemble method that uses confidence tensors to capture how each weak base classifier performs across different classes. By integrating these tensors and a smooth, partially convex objective that emphasizes margin, the method improves both classification accuracy and generalization while requiring fewer base learners. The authors also prove a property of the loss gradient that enables efficient gradient‑based optimization of the constrained problem.
arXiv:2602. 08142v2 Announce Type: replace Abstract: Machine learning applications require fast and reliable per-sample uncertainty estimation.
arXiv:2606. 28835v1 Announce Type: cross Abstract: Federated Learning (FL) emerged as a promising distributed machine learning paradigm.
arXiv:2607. 13550v1 Announce Type: cross Abstract: Boosting is one of the most successful learning techniques for standard classification and regression tasks.
arXiv:2607. 05791v1 Announce Type: cross Abstract: Boosting is a fundamental technique for generically improving the accuracy of learning algorithms (Schapire 1989).
arXiv:2606. 08797v1 Announce Type: cross Abstract: Decision-focused learning has shown great promise for addressing predict-then-optimize problems, particularly in the presence of under-specified models.
arXiv:2608. 20326v1 Announce Type: cross Abstract: Deep neural networks are often overconfident, assigning high confidence even to incorrect predictions.
arXiv:2604. 27723v2 Announce Type: replace Abstract: Learning algorithms can be significantly improved by routing complex or uncertain inputs to specialized experts, balancing accuracy with computational cost.
Deep neural networks are often overconfident, assigning high confidence even to incorrect predictions. Consequently, users lack a reliable signal for deciding when a prediction can be trusted.
arXiv:2606. 01746v1 Announce Type: cross Abstract: Modern neural networks are highly susceptible to adversarial perturbations.
Boosting is one of the most successful learning techniques for standard classification and regression tasks. Its extension to multi-output prediction problems has found an increasing number of applications in recent years.
arXiv:2607. 06772v1 Announce Type: new Abstract: Learned optimization aims to improve upon hand-designed optimizers (e.
The paper extends ex‑ante evaluation of Predict‑Then‑Optimize methods from binary to multiclass classification by simulating predictions at specified performance levels and mapping prediction errors to decision regret. It introduces a first‑order approximation that estimates regret from individual misclassifications, reducing computational effort. Experiments show the simulation accurately reproduces target performance and that the approximation is close for some problems, though it falters when simultaneous misclassifications interact significantly.