Fairness in Streaming Submodular Maximization: Algorithms and Hardness
Authors: Marwa El Halabi, Slobodan Mitrović, Ashkan Norouzi-Fard, Jakab Tardos, Jakub M. Tarnawski
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically validate our results and address the question: What is the price of fairness? To this end, we compare our approach against several baselines on four datasets. We measure: (1) Objective values. (2) Violation of fairness constraints: Given a set S, we define err(S) = P c [C] max{|S Vc| uc, ℓc |S Vc|, 0}. A single term in this sum quantifies by how many elements S violates the lower or upper bound. (3) Number of oracle calls, as is standard in the field to measure the efficiency of algorithms. |
| Researcher Affiliation | Collaboration | Marwa El Halabi MIT CSAIL marwash@mit.edu Slobodan Mitrovi c MIT CSAIL slobo@mit.edu Ashkan Norouzi-Fard Google Zurich ashkannorouzi@google.com Jakab Tardos EPFL jakab.tardos@epfl.ch Jakub Tarnawski Microsoft Research jatarnaw@microsoft.com |
| Pseudocode | Yes | Algorithm 1 FAIR-GREEDY, Algorithm 2 FAIR-STREAMING, Algorithm 3 FAIR-SAMPLE-STREAMING |
| Open Source Code | Yes | The code is available at https://github.com/google-research/google-research/tree/master/fair_submodular_maximization_2020. |
| Open Datasets | Yes | We perform experiments on the Pokec social network [41]. We use the Movielens 1M dataset [31]... We use the Census Income dataset [22]... We consider a dataset containing one record for each phone call in a marketing campaign ran by a Portuguese banking institution [49]. |
| Dataset Splits | No | The paper describes the datasets used and how fairness constraints are applied, but it does not specify explicit training, validation, and test dataset splits (e.g., percentages or sample counts) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU/CPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | While the paper describes various experimental settings for defining fairness constraints (e.g., ℓc, uc, k values), it does not provide specific hyperparameters or system-level training settings such as learning rates, batch sizes, or optimizer configurations in its main text. |