Optimal Classification under Performative Distribution Shift
Authors: Edwige Cyffers, Muni Sreenivas Pydi, Jamal Atif, Olivier Cappé
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we test the performance of our algorithm Reparametrization-based Performative Gradient (RPPerf GD) with respect to existing algorithms. Three baselines were introduced in Perdomo et al. [2020]. First, Repeated Risk Minimization (RRM) computes at each step the next θ to minimize the non-performative risk, leading to the update rule θt+1 = arg minθ DPR(θt, θ ). |
| Researcher Affiliation | Academia | Edwige Cyffers Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189 CRISt AL, F-59000 Lille edwige.cyffers@inria.fr Muni Sreenivas Pydi Université Paris Dauphine, Université PSL, CNRS, LAMSADE, 75016 Paris Jamal Atif Université Paris Dauphine, Université PSL, CNRS, LAMSADE, 75016 Paris Oliver Cappé École Normale Supérieure, Université PSL, CNRS, Inria, DI ENS, 75005 Paris |
| Pseudocode | Yes | Algorithm 1: RPPerf GD with Π learning |
| Open Source Code | No | Justification: the code will be publicly released after publication |
| Open Datasets | Yes | We use the binarized version of the Housing dataset2, where the outcome is whether the price is high or not. ... 2https://www.openml.org/d/823 |
| Dataset Splits | No | The paper mentions 'Sample size (n)' for datasets, but does not explicitly state specific training, validation, or test split percentages or sample counts in the main text or appendices for its experiments. While the NeurIPS checklist states 'Yes' for 'training and test details', the paper's content does not provide the explicit split information required by this question. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Table 1: Parameters used for figure fig. 2b Parameter Value Number of iterations (num_iter) 100 Sample size (n) 1000 Scale (σ) 0.5 Average number of iterations (num_iter_average) 100 Step size (step_size) 0.1 Regularization parameter (λ) 3 10 2 ... Table 3: Parameters used for fig. 2f Parameter Value Number of iterations (num_iter) 15 Sample size (n) 18000 Number of runs (n_runs) 20 Step size (step_size) 0.2 Regularization parameter (λ) 5 10 3 |