Performative Prediction
Authors: Juan Perdomo, Tijana Zrnic, Celestine Mendler-Dünner, Moritz Hardt
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We next examine the convergence of repeated risk minimization and repeated gradient descent in a simulated strategic classification setting. We run experiments on a dynamic credit scoring simulator in which an institution classifies the creditworthiness of loan applicants. To run our simulations, we construct a distribution map D(θ), as described in Figure 2. For the base distribution D, we use a class-balanced subset of a Kaggle credit scoring dataset (Kaggle, 2012). Figures 1 and 3 show experimental results. |
| Researcher Affiliation | Collaboration | 1University of California, Berkeley 2MH is a paid consultant for Twitter. |
| Pseudocode | Yes | Figure 2: Distribution map for strategic classification. Input: base distribution D, classifier fθ, cost function c and utility function u Sampling procedure for D(θ): 1. Sample (x, y) D 2. Compute x BR arg maxx0 u(x0, θ) c(x , x) 3. Output sample (x BR, y) |
| Open Source Code | No | The paper does not provide an explicit statement about the release of its source code or a link to a code repository. |
| Open Datasets | Yes | For the base distribution D, we use a class-balanced subset of a Kaggle credit scoring dataset (Kaggle, 2012). |
| Dataset Splits | No | The paper states it uses a "class-balanced subset of a Kaggle credit scoring dataset" but does not provide specific details on how the dataset was split into training, validation, or test sets, such as percentages, counts, or a description of cross-validation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances) used to run the experiments. |
| Software Dependencies | No | The paper mentions using a 'logistic regression classifier' but does not specify any software dependencies with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | Further details about the ex perimental setup may be found in Appendix G. In Appendix G, it states: "The weights θ are initialized to zero." "We use a logistic loss function augmented with a strongly convex regularization term of the form λ kθk22 . We used λ = 10 5 ." "We ran each experiment for 1000 iterations." "For the repeated gradient descent procedure, we used a step size η = 0.001." |