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 [1].
Rényi Fair Inference
Authors: Sina Baharlouei, Maher Nouiehed, Ahmad Beirami, Meisam Razaviyayn
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate the performance of the proposed R enyi fair inference framework in practice, we compare it with wellknown existing methods on several benchmark datasets. Experiments indicate that the proposed method has favorable empirical performance against state-of-the-art approaches. |
| Researcher Affiliation | Academia | Sina Baharlouei Industrial and Systems Engineering, USC EMAIL Maher Nouiehed Industrial Engineering and Management, AUB EMAIL Ahmad Beirami EECS, MIT EMAIL Meisam Razaviyayn Industrial and Systems Engineering, USC EMAIL |
| Pseudocode | Yes | Algorithm 1 R enyi Fair Classifier for Discrete Sensitive Attributes (...) Algorithm 2 R enyi Fair Classifier for Binary Sensitive Attributes (...) Algorithm 3 R enyi Fair K-means |
| Open Source Code | No | The paper does not provide a direct link to a code repository or explicitly state that the source code for their methodology is publicly available. |
| Open Datasets | Yes | In this section, we evaluate the performance of the proposed R enyi fair classifier and R enyi fair kmeans algorithm on three standard datasets: Bank, German Credit, and Adult datasets. The detailed description of these datasets is available in the supplementary material. All of these datasets are publicly available at UCI repository. |
| Dataset Splits | Yes | German Credit Dataset: (...) We chose first 800 customers as the training data, and last 200 customers as the test data. Bank Dataset: (...) we split data into the training (32000 data points), and test set (13211 data points). Adult Dataset: (...) The train and test sets are two separated files consisting of 32000 and 16000 samples respectively. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions implementing a "logistic regression classifier" and a "2-layers neural network" but does not specify any software names with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | In this section, we train a 2-layers neural network on the adult dataset regularized by the R enyi correlation. In this experiment, the sensitive attribute is gender. We set the number of nodes in the hidden layer, the batch-size, and the number of epochs to 12, 128, and 50, respectively. |