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..
Controllable Invariance through Adversarial Feature Learning
Authors: Qizhe Xie, Zihang Dai, Yulun Du, Eduard Hovy, Graham Neubig
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On three benchmark tasks, namely fair and bias-free classification, language-independent generation, and lighting-independent image classification, we show that the proposed framework induces an invariant representation, and leads to better generalization evidenced by the improved performance. |
| Researcher Affiliation | Academia | Qizhe Xie, Zihang Dai, Yulun Du, Eduard Hovy, Graham Neubig Language Technologies Institute Carnegie Mellon University EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our MT code is available at https://github.com/qizhex/Controllable-Invariance |
| Open Datasets | Yes | Fair Classification For fair classification, we use three datasets... The German dataset [Frank et al., 2010]... The Adult income dataset [Frank et al., 2010]... The task of the health dataset1 is to predict whether a person will spend any days in the hospital in the following year. The sensitive variable is also the age and the dataset contains 147, 473 entries. 1www.heritagehealthprize.com, Multi-lingual Machine Translation For the multi-lingual machine translation task we use French to English (fr-en) and German to English (de-en) pairs from IWSLT 2015 dataset [Cettolo et al., 2012], Image Classification We use the Extended Yale B dataset [Georghiades et al., 2001] for our image classification task. |
| Dataset Splits | Yes | We follow the same 5-fold train/validation/test splits and feature preprocessing used in [Zemel et al., 2013, Louizos et al., 2016]. and We follow Li et al. [2014], Louizos et al. [2016] s train/test split and no validation is used |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Open NMT' and 'Moses multi-bleu.perl script' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | In all of our experiments, we use Adam [Kingma and Ba, 2014] with a learning rate of 0.001., We use a batch size of 16 and the number of hidden units is set to 64. γ is set to 1 in our experiments., Every model is run for 20 epochs. γ is set to 8 and the batch size is set to 64., γ is set to 2. The batch size is set to 16 and the hidden size is set to 100. |