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 [1].
Adversarial Attacks on Linear Contextual Bandits
Authors: Evrard Garcelon, Baptiste Roziere, Laurent Meunier, Jean Tarbouriech, Olivier Teytaud, Alessandro Lazaric, Matteo Pirotta
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate the proposed approaches in synthetic and real-world datasets. |
| Researcher Affiliation | Industry | Evrard Garcelon Facebook AI Research EMAIL Baptiste Roziere Facebook AI Research EMAIL Laurent Meunier Facebook AI Research EMAIL Jean Tarbouriech Facebook AI Research EMAIL Olivier Teytaud Facebook AI Research EMAIL Alessandro Lazaric Facebook AI Research EMAIL Matteo Pirotta Facebook AI Research EMAIL |
| Pseudocode | Yes | Figure 1: Contextual ACE algorithm. Figure 2: Conic Attack algorithm. |
| Open Source Code | No | No explicit statement about releasing code for the described methodology or a link to a source code repository was found. |
| Open Datasets | Yes | In this section, we conduct experiments on the attacks on contextual bandit problems with simulated data and two real-word datasets: Movie Lens25M [32] and Jester [33]. |
| Dataset Splits | No | The paper does not explicitly provide specific details about train/validation/test dataset splits, such as percentages, sample counts, or explicit splitting methodology. It mentions using synthetic data and real-world datasets (Movie Lens25M, Jester) and running simulations. |
| Hardware Specification | No | No specific hardware details (e.g., CPU/GPU models, memory, or cluster specifications) used for running the experiments were provided in the paper. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with their version numbers (e.g., Python 3.x, PyTorch 1.y) that would allow for replication of the experimental environment. |
| Experiment Setup | Yes | As parameters, we use L = 1 for the maximal norm of the contexts, δ = 0.01, υ = σ (1 + L2(1 + |Sta|)/λ)/δ , |