Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes
Authors: Steve Hanneke
JMLR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work initiates a general study of learning and generalization without the i.i.d. assumption, starting from first principles. We specifically study universally consistent function learning, where the objective is to obtain low long-run average loss for any target function, when the data follow a given stochastic process. We are then interested in the question of whether there exist learning rules guaranteed to be universally consistent given only the assumption that universally consistent learning is possible for the given data process. [...] Our most-complete result is for the self-adaptive setting, where we propose a new learning rule and prove that it is universally consistent under every data process {Xt} for which there exist universally consistent self-adaptive learning rules. Establishing this fact requires us to develop new approaches to the design of learning algorithms. |
| Researcher Affiliation | Academia | Steve Hanneke EMAIL Toyota Technological Institute at Chicago |
| Pseudocode | No | The paper describes learning rules using mathematical equations and prose (e.g., equations 12, 33, 34, 68, 75, 78, 79, 82, 83), but does not contain explicitly labeled 'Pseudocode' or 'Algorithm' blocks with structured steps. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code, nor does it provide links to code repositories or mention code in supplementary materials. |
| Open Datasets | No | The paper focuses on theoretical aspects of learning under general stochastic processes and does not describe or use any specific datasets for empirical evaluation. Therefore, it does not provide information about publicly available datasets. |
| Dataset Splits | No | The paper does not involve empirical experiments with specific datasets, and thus does not provide any information about training, test, or validation dataset splits. |
| Hardware Specification | No | This paper is theoretical in nature and does not report on any experimental results. Consequently, it does not provide specifications for any hardware used in experiments. |
| Software Dependencies | No | The paper is a theoretical work and does not describe experimental implementations or software dependencies with version numbers. |
| Experiment Setup | No | As a theoretical paper, it does not detail any experimental setup, hyperparameters, model initialization, or training schedules. It focuses on the mathematical properties and proofs of learning rules. |