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..
Explicit Defense Actions Against Test-Set Attacks
Authors: Scott Alfeld, Xiaojin Zhu, Paul Barford
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using these methods, we perform an empirical investigation of optimal defense actions for a particular class of linear models autoregressive forecasters and find that for ten real world futures markets, the optimal defense action reduces the Bob s loss by between 78 and 97%. |
| Researcher Affiliation | Collaboration | Scott Alfeld, Xiaojin Zhu, Paul Barford Department of Computer Sciences University of Wisconsin Madison Madison WI 53706, USA com Score, Inc. 11950 Democracy Drive, Suite 600 Reston, VA 20190, USA. |
| Pseudocode | No | The paper does not contain explicitly labeled |
| Open Source Code | No | The paper does not provide an explicit statement about open-source code availability or a link to a repository. |
| Open Datasets | Yes | Data is freely available from www.quandl.com. Identification codes for individual datasets are provided in Figure 1. |
| Dataset Splits | No | The paper does not explicitly state training/validation/test dataset splits. It mentions |
| Hardware Specification | No | The paper mentions that |
| Software Dependencies | Yes | All figures were made with Matplotlib (Hunter 2007) v 1.5.1. |
| Experiment Setup | No | The paper mentions specific experimental settings like |