Agnostic Insurability of Model Classes
Authors: Narayana Santhanam, Venkat Anantharam
JMLR 2015 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We completely characterize the insurability of any class P of distributions over natural numbers by means of a condition on how the neighborhoods of distributions in P should be, one that is both necessary and sufficient. Our main result is Theorem 1 of Section 3, which states that P is insurable iffit has no deceptive distributions. We prove this theorem in Sections 5 and 6. |
| Researcher Affiliation | Academia | Narayana Santhanam EMAIL Dept of Electrical Engineering University of Hawaii at Manoa Honolulu, HI 96822 Venkat Anantharam EMAIL EECS Dept University of California, Berkeley Berkeley, CA 94720 |
| Pseudocode | No | The paper describes theoretical concepts, definitions, theorems, and proofs related to the insurability of model classes. It does not contain any sections explicitly labeled 'Pseudocode' or 'Algorithm', nor does it present structured, code-like procedural steps. |
| Open Source Code | No | The paper does not contain any statements about releasing source code, providing repository links, or including code in supplementary materials. |
| Open Datasets | No | Motivated by problems in insurance, our task is to predict finite upper bounds on a future draw from an unknown distribution p over natural numbers. The paper discusses theoretical model classes and probability distributions over natural numbers, without using or referencing any specific publicly available or open datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments performed on datasets; therefore, it does not provide information about training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on mathematical proofs and characterizations of model classes. It does not describe any experiments that would require specific hardware, and thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is a theoretical work presenting mathematical concepts and proofs. It does not describe any software implementations or experiments that would necessitate listing specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is entirely theoretical, focusing on mathematical definitions, theorems, and proofs related to the insurability of model classes. Consequently, it does not include any details regarding experimental setups, hyperparameters, or system-level training settings. |