Adversarial Attacks on Combinatorial Multi-Armed Bandits
Authors: Rishab Balasubramanian, Jiawei Li, Prasad Tadepalli, Huazheng Wang, Qingyun Wu, Haoyu Zhao
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theoretical findings via extensive experiments on real-world CMAB applications including probabilistic maximum covering problem, online minimum spanning tree, cascading bandits for online ranking, and online shortest path. Finally, numerical experiments 1 are conducted to verify our theory (Section 5). |
| Researcher Affiliation | Academia | 1Oregon State University 2University of Illinois Urbana-Champaign 3Pennsylvania State University 4Princeton University. |
| Pseudocode | Yes | Algorithm 1 Attack algorithm for CMAB instance |
| Open Source Code | Yes | Finally, numerical experiments 1 are conducted to verify our theory (Section 5). Specifically, we validated that our attack algorithm efficiently attacked the applications mentioned above in unknown environments. 1Code can be found at https://github.com/ haoyuzhao123/robust-cmab-code |
| Open Datasets | Yes | In our study, we utilize the Flickr dataset (Mc Auley & Leskovec, 2012) for the probabilistic maximum coverage, online minimum spanning tree, and online shortest path problems. For cascading bandits, we employ the Movie Lens small dataset (Harper & Konstan, 2015), which comprises 9,000 movies. |
| Dataset Splits | No | The paper mentions using the Flickr and Movie Lens datasets and describes some data preparation steps (e.g., 'downsample a subgraph', 'randomly sample 5,000 movies'), but it does not specify explicit training, validation, or test dataset splits (e.g., 80/10/10 split percentages or sample counts) for reproducibility. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments, such as specific GPU/CPU models, memory, or cloud computing instance types. |
| Software Dependencies | No | The paper does not provide a reproducible description of ancillary software, as it does not list specific version numbers for key software components or libraries (e.g., Python, PyTorch, or specialized solvers). |
| Experiment Setup | Yes | Probabilistic maximum coverage We use the CUCB algorithm (Chen et al., 2016) along with a greedy oracle. We consider two kinds of targets: In the first case, we calculate the average weight of all outgoing edges of a node, sort the nodes with decreasing average weight, and select the nodes K + 1, . . . , 2K, and the selected node set is denoted fixed target. The second type is the random target, where we randomly sample K nodes whose average weight over all the outgoing edges is greater than 0.5 to ensure no node with extremely sparse edge activation is selected. Online shortest path... We set the threshold θ as 0.5. |