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..
Adversarial Regression with Multiple Learners
Authors: Liang Tong, Sixie Yu, Scott Alfeld, vorobeychik
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted experiments on three datasets: Wine Quality (redwine),PDF malware (PDF), and Boston Housing Market (boston). |
| Researcher Affiliation | Academia | 1Department of EECS, Vanderbilt University, Nashville, TN, USA 2Computer Science Department, Amherst College, Amherst, MA, USA. |
| Pseudocode | No | The paper describes computational methods and proofs, but it does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor does it present structured steps in a code-like format. |
| Open Source Code | No | The paper refers to an open-sourced tool 'mimicus' (https://github.com/srndic/mimicus) used for data extraction, but it does not provide a link or explicit statement about the availability of the authors' own source code for the methodology described in the paper. |
| Open Datasets | Yes | We conducted experiments on three datasets: Wine Quality (redwine),PDF malware (PDF), and Boston Housing Market (boston). The Wine Quality dataset (Cortez et al., 2009) |
| Dataset Splits | No | The paper states: 'The dataset is equally divided into a training set (Xtrain, ytrain) and a testing set (Xtest, ytest).' It does not explicitly mention or detail a separate validation set split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU/GPU models, memory, or specific computing environments with specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions tools like 'mimicus' and 'peepdf' but does not provide specific version numbers for these or any other software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | Remember that in Eq.(11) there are three hyper-parameters in the defender s loss function: λ, β, and z. λ is the regularization coefficient in the attacker s loss function shown in Eq.(4). It is negatively proportional to the attacker s strength. β is the probability of a test data being malicious. z is the predication targets of the attacker. [...] We denote by ˆλ = 0.5 and ˆβ = 0.8 the defender s estimates of the true λ and β. [...] We let = 5σr 1, where 1 is a vector with all elements equal to one. [...] The number of learners is set to 5. [...] The regularization parameters of Lasso and Ridge were selected by cross-validation. |