Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers
Authors: Jeffrey Negrea, Blair Bilodeau, Nicolò Campolongo, Francesco Orabona, Dan Roy
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 jeffrey.negrea@mail.utoronto.ca Blair Bilodeau University of Toronto blair.bilodeau@mail.utoronto.ca Nicolò Campolongo Spanflug Technologies Gmb H nico.campolongo@spanflug.de Francesco Orabona Boston University francesco@orabona.com Daniel M. Roy University of Toronto daniel.roy@utoronto.ca |
| 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. |