Information from coincidences

arXiv:2606.25042 · cs.IT, math.PR, math.ST, stat.ML · Submitted 2026-06-23 · Read on arXiv

Akshay Balsubramani

cs.IT, math.PR, math.ST, stat.ML

Submitted: 2026-06-23

Comments: 78 pages, 16 figures, 7 tables. Submitted to NeurIPS 2026. A mixed coincidence partition function gives Sanov, Chernoff, PAC-Bayes, and Renyi as corollaries

Code: https://github.com/GET-Foundation/get_model

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

Terminology

Benchmark — 69× in this paper · explained in 2 episodes
Trace — 53× in this paper · explained in 2 episodes
AUC — 40× in this paper · explained in 2 episodes
Ablation — 12× in this paper · explained in 1 episode
Composition — 11× in this paper · explained in 2 episodes
Monotonicity — 11× in this paper · explained in 2 episodes
Generalization — 9× in this paper · explained in 14 episodes
Core — 8× in this paper · explained in 2 episodes
Error Exponents — 7× in this paper · explained in 2 episodes
Mutual Information — 7× in this paper · explained in 2 episodes
Parameterization — 6× in this paper · explained in 2 episodes
Verification — 6× in this paper · explained in 2 episodes
Quantization — 5× in this paper · explained in 15 episodes
Data Processing Inequality — 4× in this paper
Permutation Invariance — 4× in this paper · explained in 2 episodes
Accuracy — 3× in this paper · explained in 1 episode
Axioms — 3× in this paper · explained in 2 episodes
Consistency — 3× in this paper · explained in 3 episodes
Fidelity — 3× in this paper · explained in 2 episodes
Foundation Model — 3× in this paper · explained in 3 episodes
Localization — 3× in this paper · explained in 2 episodes
Sample Complexity — 3× in this paper · explained in 20 episodes
Uncertainty Quantification — 3× in this paper · explained in 9 episodes
Embeddings — 2× in this paper · explained in 2 episodes
Failure Modes — 2× in this paper · explained in 3 episodes
Generalization Bounds — 2× in this paper · explained in 2 episodes
Hallucination — 2× in this paper · explained in 4 episodes
Intrinsic Dimension — 2× in this paper · explained in 3 episodes
Meta-learning — 2× in this paper · explained in 3 episodes
Scaling Laws — 2× in this paper · explained in 4 episodes
Variational Bayes — 2× in this paper · explained in 2 episodes
Admissibility — 1× in this paper · explained in 2 episodes
Benchmarking — 1× in this paper · explained in 4 episodes
Continual Learning — 1× in this paper · explained in 3 episodes
Degeneracy — 1× in this paper · explained in 4 episodes
Domain Adaptation — 1× in this paper · explained in 5 episodes
Exchangeability — 1× in this paper · explained in 2 episodes
Fine-Tuning — 1× in this paper · explained in 5 episodes
KKT conditions — 1× in this paper · explained in 2 episodes
Kullback–Leibler Divergence — 1× in this paper · explained in 2 episodes
LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
Mixture of Experts — 1× in this paper · explained in 2 episodes
Regret — 1× in this paper · explained in 4 episodes
Structural Properties — 1× in this paper · explained in 2 episodes

Information from coincidences

Terminology

Sources

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