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..
Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back
Authors: Vitaly Feldman
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work we substantially strengthen the lower bound in [18] proving that a linear dependence on the dimension d is necessary for ERM (and, consequently, uniform convergence). We then extend the lower bound to all ℓp/ℓq setups and examine several related questions. Finally, we examine a more general setting of bounded-range SCO (that is |f(x)| 1 for all x K). |
| Researcher Affiliation | Industry | Vitaly Feldman IBM Research Almaden |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention releasing source code for its methodology. |
| Open Datasets | No | This is a theoretical paper and does not involve training on datasets. |
| Dataset Splits | No | This is a theoretical paper and does not involve dataset splits for validation. |
| Hardware Specification | No | The paper does not describe experiments and therefore provides no hardware specifications. |
| Software Dependencies | No | The paper does not describe experiments and therefore provides no software dependencies. |
| Experiment Setup | No | The paper does not describe experiments and therefore provides no experimental setup details. |