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:2610.32940v1 Announce Type: cross Abstract: We study the fundamental problem of PAC learning positive linear combinations of $k$ arbitrary ReLU activations on $\mathbb{R}^d$ with respect to the...
arXiv:2602. 08142v2 Announce Type: replace Abstract: Machine learning applications require fast and reliable per-sample uncertainty estimation.
arXiv:2609.24328v1 Announce Type: new Abstract: Combining predictions from different models can improve performance at machine learning tasks, but the training of the individual models and the rule u...
arXiv:2606. 28835v1 Announce Type: cross Abstract: Federated Learning (FL) emerged as a promising distributed machine learning paradigm.
arXiv:2604. 15613v4 Announce Type: replace-cross Abstract: We present Green-ELM, a non-iterative neural architecture that replaces gradient-based optimization of the output layer with a closed-form analytic solution over a fixed, high-dimensional random feature representation.
arXiv:2610.28025v1 Announce Type: cross Abstract: This paper studies the problem of aggregation in multiclass classification, generalizing existing results for binary classification. Given a finite d...
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.
Optimizable Model Averaging (OMA) is a black‑box method that assigns nonnegative weights to each candidate model’s predictive probabilities, keeping all model parameters frozen. By minimizing regularized predictive entropy, OMA learns a single scalar coefficient per model to average outputs from heterogeneous architectures. The authors show that appropriate regularization reduces expected calibration error and improves the trade‑off between performance and calibration compared to simple output averaging.