Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Universal Rates of Empirical Risk Minimization
Authors: Steve Hanneke, Mingyue Xu
NeurIPS 2024 | Venue PDF | 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 EMAIL Mingyue Xu Department of Computer Science Purdue University EMAIL |
| 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. |