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 [1].
Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity
Authors: Christian Fröhlich, Robert Williamson
TMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We restrict ourselves to theoretical analysis as there is ample experimental evidence for the suitability of risk measures in different settings in the aforementioned works. In addition, Chouzenoux et al. (2019) and Fröhlich & Williamson (2022) provide general treatments on risk measures in machine learning, including experimental evidence. Our focus is therefore on drawing connections and deepening our theoretical understanding. For aspects regarding practical computation see Section 8. |
| Researcher Affiliation | Academia | Christian Fröhlich EMAIL Department of Computer Science University of Tübingen Robert C. Williamson EMAIL Department of Computer Science and Tübingen AI center University of Tübingen |
| Pseudocode | No | The paper provides mathematical definitions, propositions, theorems, proofs, and theoretical discussions. There are no explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions 'Reviewed on Open Review: https: // openreview. net/ forum? id= Unt Uoe Lwwu' which is a review platform, not a code repository. No other statements regarding the release of source code for the methodology described in this paper were found. |
| Open Datasets | No | The paper is theoretical and does not present experimental results based on specific datasets. Therefore, no datasets are used or made publicly available by the authors within the scope of this paper. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments using datasets. Therefore, there is no mention of dataset splits such as training, validation, or test sets. |
| Hardware Specification | No | The paper focuses on theoretical analysis and does not describe any experiments that would require specific hardware. Consequently, no hardware specifications are provided. |
| Software Dependencies | No | The paper presents theoretical work and does not describe any experimental implementations that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is purely theoretical, focusing on mathematical concepts and relationships. It does not include any experimental setup details such as hyperparameters, model initialization, or training schedules. |