On the Learnability of Multilabel Ranking
Authors: Vinod Raman, UNIQUE SUBEDI, Ambuj Tewari
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 vkraman@umich.edu Unique Subedi Department of Statistics University of Michigan Ann Arbor, MI 48104 subedi@umich.edu Ambuj Tewari Department of Statistics University of Michigan Ann Arbor, MI 48104 tewaria@umich.edu |
| 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. |