Regret Bounds for Non-decomposable Metrics with Missing Labels
Authors: Nagarajan Natarajan, Prateek Jain
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We corroborate our theoretical findings with experimental evaluation on several real-world multi-label datasets, demonstrating the efficacy of our proposed framework for handling missing labels. |
| Researcher Affiliation | Academia | Weiwei Liu, Ivor Tsang, University of New South Wales, Australia, University of Technology Sydney, Australia |
| Pseudocode | Yes | Algorithm 1: Stochastic Online Learning for Missing Label Problems |
| Open Source Code | No | The paper does not provide an explicit statement about the release of open-source code or a link to a code repository. |
| Open Datasets | Yes | We conduct experiments on several real-world multi-label datasets including Bibtex, Delicious, EUR-Lex, RCV1-v2, Wiki, and LSHTC-Large. All datasets are publicly available from [37, 24, 23]. |
| Dataset Splits | No | For each dataset, we randomly split the data into 80% for training and 20% for testing. There is no explicit mention of a validation set or its specific split details. |
| Hardware Specification | Yes | All experiments are performed on a single machine with 64 Intel(R) Xeon(R) CPU E5-2699 v3 @ 2.30GHz and 1TB memory. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We choose the learning rate η by tuning hyperparameters using cross-validation on the training set. The regularization parameter λ is set to 1/n. |