Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
One-Shot Strategic Classification Under Unknown Costs
Authors: Elan Rosenfeld, Nir Rosenfeld
ICML 2024 | Venue PDF | 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 <EMAIL>. |
| 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. |