Achieving More with Less: A Tensor-Optimization-Powered Ensemble Method

arXiv:2408.02936 · cs.LG · Submitted 2024-08-06 · Read on arXiv

cs.LG

Submitted: 2024-08-06

Updated: 2026-09-02

Comments: Correct typesetting and other errors

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 29× in this paper · explained in 1 episode
• Loss Function — 17× in this paper · explained in 2 episodes
• Ensemble Learning — 12× in this paper · explained in 2 episodes
• Objective Function — 10× in this paper · explained in 2 episodes
• Generalization — 7× in this paper · explained in 13 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Adaptivity — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Non-stationarity — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Spectral clustering — 1× in this paper · explained in 2 episodes
• Support Vector Classification — 1× in this paper

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks