One-Shot Strategic Classification Under Unknown Costs
Authors: Elan Rosenfeld, Nir Rosenfeld
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We design efficient algorithms for both the full-batch and stochastic settings, which we prove converge (offline) to the minimax solution at the rate of O(T 1/2). Our analysis reveals important structure stemming from strategic responses, particularly the value of dual norm regularization with respect to the cost function. |
| Researcher Affiliation | Academia | 1Carnegie Mellon University 2Technion Israel Institute of Technology. Correspondence to: Elan Rosenfeld <elan@cmu.edu>. |
| Pseudocode | Yes | Algorithm 1 Pseudocode for MAXLOSSCOST Algorithm 2 Stochastic Mirror Descent-Ascent on the regularized strategic hinge loss Algorithm 3 Subgradient method on k-shifted strategic hinge loss |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | This paper is theoretical and does not conduct experiments on datasets, thus it does not mention public or open datasets for training. |
| Dataset Splits | No | This paper is theoretical and does not conduct empirical experiments; therefore, it does not describe training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and proofs; it does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe experimental implementations or software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and proofs; it does not describe an experimental setup with hyperparameters or system-level training settings. |