Outside the Echo Chamber: Optimizing the Performative Risk
Authors: John P Miller, Juan C Perdomo, Tijana Zrnic
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical findings with an empirical evaluation of different methods on two tasks: the strategic classification simulator from Perdomo et al. (2020), and a synthetic linear regression example. ... For each of the following experiments, we run each algorithm 50 times and display 95% bootstrap confidence intervals. |
| Researcher Affiliation | Academia | 1University of California, Berkeley. Correspondence to: John Miller <miller john@berkeley.edu>, Juan C. Perdomo <jcperdomo@berkeley.edu>, Tijana Zrnic <tijana.zrnic@berkeley.edu>. |
| Pseudocode | Yes | Algorithm 1 Two-Stage Algorithm for Location Families |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the code for the work described in this paper, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | We complement our theoretical findings with an empirical evaluation of different methods on two tasks: the strategic classification simulator from Perdomo et al. (2020), and a synthetic linear regression example. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | We provide a formal description of all the procedures, as well as a detailed description of the experimental setup in Appendix D. Appendix D itself details parameters like 'x ~ N(0, Σx), U_y ~ N(0, σ^2_y), y = β^T x + µ^T θ + U_y' for linear regression, and 'add ℓ2-regularization to the logistic loss' for strategic classification. |