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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Consistent algorithms for multi-label classification with macro-at-$k$ metrics
Authors: Erik Schultheis, Wojciech Kotlowski, Marek Wydmuch, Rohit Babbar, Strom Borman, Krzysztof Dembczynski
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results provide evidence for the competitive performance of the proposed approach. ... In this section, we empirically evaluate the proposed Frank-Wolfe algorithm on a variety of multi-label benchmark tasks... |
| Researcher Affiliation | Collaboration | Erik Schultheis Aalto University Helsinki, Finland EMAIL Wojciech Kotłowski Poznan University of Technology Poznan, Poland EMAIL Marek Wydmuch Poznan University of Technology Poznan, Poland EMAIL Rohit Babbar University of Bath / Aalto University Bath, UK / Helsinki, Finland EMAIL Strom Borman Yahoo Research Champaign, USA EMAIL Krzysztof Dembczy nski Yahoo Research / Poznan University of Technology New York, USA / Poznan, Poland EMAIL |
| Pseudocode | Yes | Algorithm 1 Multi-label Frank-Wolfe algorithm for complex performance measures |
| Open Source Code | Yes | Code to reproduce the experiments: https://github.com/mwydmuch/xCOLUMNs |
| Open Datasets | Yes | In this section, we empirically evaluate the proposed Frank-Wolfe algorithm on a variety of multi-label benchmark tasks that differ substantially in the number of labels and imbalance of the label distribution: MEDIAMILL (Snoek et al., 2006), FLICKR (Tang & Liu, 2009), RCV1X (Lewis et al., 2004), and AMAZONCAT (Mc Auley & Leskovec, 2013; Bhatia et al., 2016). |
| Dataset Splits | Yes | In the beginning, we split the available training data into two subsets. One for estimating label probabilities bη, and one for tuning the actual classifier. ... we tested different ratios (50/50 or 75/25) of splitting training data into sets used for training the label probability estimators and estimating confusion matrix C, as well as a variant where we use the whole training set for both steps. ... Table 2: Results of different inference strategies on measure calculated at {3, 5, 10}. Notation: P precision, R recall, F1 F1-measure. ... MEDIAMILL (m = 101, ntrain = 30993, ntest = 12914,...) |
| Hardware Specification | Yes | All the experiments were conducted on a workstation with 64 GB of RAM and Nvidia V100 16Gb GPU. |
| Software Dependencies | No | The paper mentions 'implemented in Pytorch Paszke et al. (2019)' and 'We use Adam optimizer (Kingma & Ba, 2015)' but does not provide specific version numbers for Pytorch or Adam, only the publication years of the papers describing them. |
| Experiment Setup | Yes | For the first three datasets we use a multi-layer neural network for estimating bη(x). For the last and largest dataset, we use a sparse linear label tree model... We use k = 200 for both RCV1X and AMAZONCAT datasets. ... We tested different ratios (50/50 or 75/25) of splitting training data... We also investigated two strategies for initialization of classifier h by either using equal weights (resulting in a TOP-K classifier) or random weights. Finally, we terminate the algorithm if we do not observe sufficient improvement in the objective. In practice, we found that Frank-Wolfe converges within 3-10 iterations. |