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..
Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers
Authors: Jeffrey Negrea, Blair Bilodeau, Nicolò Campolongo, Francesco Orabona, Dan Roy
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | If you ran experiments... (c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)? [N/A] All the experiments are deterministic (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A] All computations were done on CPU on a personal laptop computer. |
| Researcher Affiliation | Collaboration | Jeffrey Negrea University of Toronto EMAIL Blair Bilodeau University of Toronto EMAIL Nicolò Campolongo Spanflug Technologies Gmb H EMAIL Francesco Orabona Boston University EMAIL Daniel M. Roy University of Toronto EMAIL |
| Pseudocode | No | The paper describes algorithms using mathematical equations and textual explanations, but it does not include a distinct 'Pseudocode' block or 'Algorithm' figure. |
| Open Source Code | No | The paper states 'All computations were done on CPU on a personal laptop computer.' and answers 'Yes' to 'Did you include the code... needed to reproduce the main experimental results', but provides no specific link or explicit statement about the open-sourcing of the described FTRL algorithm's implementation code or the root-logarithmic regularizers. |
| Open Datasets | No | The paper is theoretical and does not involve the use of specific datasets for training or evaluation. Therefore, it does not provide information about publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets, thus no dataset splits for training, validation, or testing are mentioned. |
| Hardware Specification | No | The paper states 'All computations were done on CPU on a personal laptop computer,' but does not provide specific details such as the CPU model, memory, or other hardware specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experimental setup with specific hyperparameters, training configurations, or system-level settings. |