Optimal Allocation of Embedding Dimensions under Finite-Sample Constraints

arXiv:2608.24592 · math.OC, stat.ME, stat.ML · Submitted 2026-08-25 · Read on arXiv

math.OC, stat.ME, stat.ML

Submitted: 2026-08-25

Updated: 2026-08-25

Comments: Keywords: Machine Learning in OR, Constrained Optimization, Nonlinear Programming, Multivariate Statistics, Predictive Models

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

Terminology

Embeddings — 20× in this paper · explained in 2 episodes
Accuracy — 11× in this paper · explained in 1 episode
Calibration — 6× in this paper · explained in 7 episodes
Generalization — 6× in this paper · explained in 13 episodes
Benchmark — 4× in this paper · explained in 2 episodes
Regularization — 4× in this paper · explained in 3 episodes
Latent Space — 3× in this paper · explained in 7 episodes
Reconstruction Error — 3× in this paper · explained in 2 episodes
Data Heterogeneity — 2× in this paper · explained in 3 episodes
F1 Score — 2× in this paper · explained in 3 episodes
Latent Representation — 2× in this paper · explained in 2 episodes
Loss Function — 2× in this paper · explained in 2 episodes
Predictive Modeling — 2× in this paper · explained in 2 episodes
Representation Learning — 2× in this paper · explained in 2 episodes
Abstraction — 1× in this paper · explained in 1 episode
Adam — 1× in this paper · explained in 2 episodes
Ambient Dimension — 1× in this paper · explained in 1 episode
Bayesian Optimization — 1× in this paper · explained in 3 episodes
Benchmarking — 1× in this paper · explained in 3 episodes
Core — 1× in this paper · explained in 2 episodes
Curse of Dimensionality — 1× in this paper · explained in 6 episodes
Federated Learning — 1× in this paper · explained in 14 episodes
Fidelity — 1× in this paper · explained in 2 episodes
Resource Constraints — 1× in this paper · explained in 3 episodes
Transfer Learning — 1× in this paper · explained in 10 episodes
Transformer — 1× in this paper · explained in 2 episodes

Terminology

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