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..
Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach
Authors: Cuong Tran, Ferdinando Fioretto, Pascal Van Hentenryck9932-9939
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The paper analyses the tension between accuracy, privacy, and fairness and the experimental evaluation illustrates the benefits of the proposed model on several prediction tasks. |
| Researcher Affiliation | Academia | Syracuse University 2 Georgia Institute of Technology |
| Pseudocode | Yes | Algorithm 1: Fair-Lagrangian Dual (F-LD) |
| Open Source Code | No | The paper does not provide an explicit statement or link to its own open-source code for the described methodology. |
| Open Datasets | Yes | This section studies the behavior of the proposed algorithm on several datasets, including Income, Bank, and Compas (Zafar et al. 2017a) datasets. |
| Dataset Splits | No | The paper mentions 'Training data' and 'mini-batch B', but it does not provide specific details on validation splits (e.g., percentages, sample counts, or explicit mention of a validation set). |
| Hardware Specification | Yes | The tests use a common laptop (Mac Book Air 2013, 1.7GHz, 8GB RAM) on the Bank dataset and are consistent for all the fairness notions adopted. |
| Software Dependencies | No | The paper mentions using 'JAX' for speedups, but it does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | PF-LD uses clipping bound values Cp = 10.0 and Cd = 5.0 and each experiment and configuration is repeated 10 times and presents average and standard deviation results. The privacy losses are set to ϵ = 1.0 and δ = 10-5, unless otherwise specified. Given the input dataset D, the optimizer step size α > 0, and the vector of step sizes s, the Lagrangian multipliers are initialized in line 1. The training is performed for a fixed number of T epochs. At each epoch k, the primal update step (lines 3 and 4) optimizes the model parameters θ using stochastic gradient descent over different mini-batches B D. |