A universally consistent learning rule with a universally monotone error
Authors: Vladimir Pestov
JMLR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our aim is to show that smart universally consistent rules do exist, even without requiring any amount of randomization. We use a partitioning rule: the domain is divided in disjoint cells, and the label for each cell is determined by the majority vote among all datapoints contained in it. The sections 2 5 lay the technical groundwork for our learning rule, presented and studied in the sections 6 8. In Sect. 7 we show the rule has a universally monotone expected error, and in Sect. 8 we prove the universal consistency. |
| Researcher Affiliation | Academia | Vladimir Pestov EMAIL Departamento de Matem atica Universidade Federal de Santa Catarina Campus Universit ario Trindade CEP 88.040-900 Florian opolis-SC, Brasil and Departement of Mathematics and Statistics University of Ottawa STEM Complex, 150 Louis-Pasteur Pvt Ottawa, Ontario K1N 6N5 Canada |
| Pseudocode | Yes | Here is the algorithm description. on input σn do k max{i: ni n} Q R for i = 1 : k do if every interval I ˆPi contains ai points of σ[Bi] and (i = 1 or every interval I ˆQ contains Ni points of σ[Ai]) do if k > 1 do for every I ˆQ do if Pσ[Ai][Y = 1|X I] (ϵi, 1 ϵi), do R R (Pi I) end do end if end do end for end if Q R H h ˆ Q(σ[Bi]) end do end if end for end do return H |
| Open Source Code | No | No explicit statement or link regarding source code availability is provided in the paper. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on specific datasets. It discusses 'data distribution' and 'labelled n-sample' in a general theoretical context without providing concrete access information for any open datasets. |
| Dataset Splits | No | The paper describes a theoretical learning rule and does not include experimental evaluations on specific datasets, therefore no dataset split information is provided. |
| Hardware Specification | No | The paper presents a theoretical learning rule and does not include an experimental section, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe an implementation or any software used, so no software dependencies are listed. |
| Experiment Setup | No | The paper is theoretical and does not present experimental results, therefore no experimental setup details such as hyperparameters or training configurations are provided. |