Cheap and Powerful Tests for Supervised Subspaces: Per-Component Inference for PLS

arXiv:2609.36307 · cs.LG, stat.ML · Submitted 2026-09-28 · Read on arXiv

cs.LG, stat.ML

Submitted: 2026-09-28

Updated: 2026-09-28

Code: https://github.com/crscardellino/sbwce

Project page: https://crscardellino.github.io/SBWCE

Terminology

• Calibration — 45× in this paper · explained in 7 episodes
• Embeddings — 17× in this paper · explained in 2 episodes
• Partial Least Squares — 11× in this paper
• Benchmark — 7× in this paper · explained in 2 episodes
• alpha — 5× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Linear probe — 3× in this paper · explained in 2 episodes
• Parsimony — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• Exchangeability — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Provenance — 2× in this paper · explained in 4 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Anisotropy — 1× in this paper · explained in 3 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• EMMA — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 1× in this paper
• International Conference on Machine Learning — 1× in this paper
• Meeting of the Association for Computational Linguistics — 1× in this paper
• Representation Learning — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

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