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..
Group-wise oracle-efficient algorithms for online multi-group learning
Authors: Samuel Deng, Jingwen Liu, Daniel J. Hsu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is a theory paper, where the main contributions are towards novel algorithmic design principles for a learning-theoretic model. |
| Researcher Affiliation | Academia | Samuel Deng Department of Computer Science Columbia University EMAIL Daniel Hsu Department of Computer Science Columbia University EMAIL Jingwen Liu Department of Computer Science Columbia University EMAIL |
| Pseudocode | Yes | Algorithm 1: Algorithm for Group-wise Oracle Efficiency (for smoothed online learning) |
| Open Source Code | No | The paper does not include any explicit statement about releasing its own source code, nor does it provide a link to a code repository. As stated in the NeurIPS checklist, this is a theory paper. |
| Open Datasets | No | The paper mentions 'n i.i.d. training samples' in a theoretical context, but does not provide any concrete access information, citations, or links to a publicly available dataset. As stated in the NeurIPS checklist, this is a theory paper and does not involve empirical studies with specific datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe any experimental procedures or dataset splits. No specific details about validation splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not list specific software dependencies with version numbers required for replicating experiments. |
| Experiment Setup | No | The paper is theoretical and does not provide concrete hyperparameter values, training configurations, or system-level settings for an experimental setup. |