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].
On the Learnability of Multilabel Ranking
Authors: Vinod Raman, UNIQUE SUBEDI, Ambuj Tewari
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we characterize the learnability of multilabel ranking problems in both batch and online settings for a large family of ranking losses. Along the way, we give two equivalence classes of ranking losses based on learnability that capture most losses used in practice. |
| Researcher Affiliation | Academia | Vinod Raman Department of Statistics University of Michigan Ann Arbor, MI 48104 EMAIL Unique Subedi Department of Statistics University of Michigan Ann Arbor, MI 48104 EMAIL Ambuj Tewari Department of Statistics University of Michigan Ann Arbor, MI 48104 EMAIL |
| Pseudocode | Yes | Algorithm 2 Expert (b, φ); Algorithm 3 Agnostic Online Learner Q for H w.r.t. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, nor does it include links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe experiments that use datasets, nor does it provide information about the public availability of any dataset. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets, thus no details on training/test/validation splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper focuses on theoretical characterization and does not provide specific software dependencies or version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, hyperparameters, or training configurations. |