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..
Extreme Classification via Adversarial Softmax Approximation
Authors: Robert Bamler, Stephan Mandt
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated the proposed adversarial negative sampling method on two established benchmarks by comparing speed of convergence and predictive performance against five different baselines. Figure 1 shows our results on the Wikipedia-500K data set (left two plots) and the Amazon670K data set (right two plots). |
| Researcher Affiliation | Academia | Robert Bamler & Stephan Mandt Department of Computer Science University of California, Irvine EMAIL |
| Pseudocode | No | The paper describes the algorithm in prose, but does not provide a formal pseudocode or algorithm block with a dedicated label. |
| Open Source Code | Yes | and we publish the code1 of both the main and the auxiliary model. 1https://github.com/mandt-lab/adversarial-negative-sampling |
| Open Datasets | Yes | We used the Wikipedia-500K and Amazon-670K data sets from the Extreme Classification Repository (Bhatia et al.) with K = 512-dimensional XML-CNN features (Liu et al., 2017) downloaded from (Saxena). |
| Dataset Splits | Yes | We tuned the hyperparameters for each method individually using the validation set. We split off 10% of the training set for validation, and report results on the provided test set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of an Adagrad optimizer but does not provide specific version numbers for software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | Table 1 shows the resulting hyperparameters. For the proposed method and baselines (i)-(iii) we used an Adagrad optimizer (Duchi et al., 2011) and considered learning rates ρ {0.0003, 0.001, 0.003, 0.01, 0.03} and regularizer strengths (see Eq. 6) λ {10 5, 3 10 5, . . . , 0.03}. |