Universal Rates of Empirical Risk Minimization
Authors: Steve Hanneke, Mingyue Xu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is a fundamental tetrachotomy: there are only four possible universal learning rates by ERM, namely, the learning curves of any concept class learnable by ERM decay either at e n, 1/n, log (n)/n, or arbitrarily slow rates. Moreover, we provide a complete characterization of which concept classes fall into each of these categories, via new complexity structures. We also develop new combinatorial dimensions which supply sharp asymptotically-valid constant factors for these rates, whenever possible. |
| Researcher Affiliation | Academia | Steve Hanneke Department of Computer Science Purdue University steve.hanneke@gmail.com Mingyue Xu Department of Computer Science Purdue University xu1864@purdue.edu |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled 'Pseudocode' or 'Algorithm', nor does it present structured algorithm blocks. |
| Open Source Code | No | The paper is theoretical in nature and does not mention releasing any source code for its methodology or provide links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve experimental evaluation on datasets, so it does not discuss public dataset availability for training. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental evaluation or dataset splitting, so it does not provide training/validation/test splits. |
| Hardware Specification | No | The paper is theoretical and does not report on experimental setups or hardware used for computation. |
| Software Dependencies | No | The paper is theoretical and does not report on experimental setups or software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include experimental details such as hyperparameters or system-level training settings. |