Combinatorial Cascading Bandits
Authors: Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvari
NeurIPS 2015 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Comb Cascade on two real-world problems and show that it performs well even when our modeling assumptions are violated. We also demonstrate that our setting requires a new learning algorithm. We evaluate Comb Cascade in three experiments. In Section 4.1, we compare it to Comb UCB1 [12], a state-of-the-art algorithm for stochastic combinatorial semi-bandits with a linear reward function. This experiment shows that Comb UCB1 cannot solve all instances of our problem, which highlights the need for a new learning algorithm. It also shows the limitations of Comb Cascade. We evaluate Comb Cascade on two real-world problems in Sections 4.2 and 4.3. |
| Researcher Affiliation | Collaboration | Branislav Kveton Adobe Research San Jose, CA EMAIL Azin Ashkan Technicolor Research Los Altos, CA EMAIL Zheng Wen Yahoo Labs Sunnyvale, CA EMAIL Csaba Szepesvari Department of Computing Science University of Alberta EMAIL |
| Pseudocode | Yes | Algorithm 1 Comb Cascade for combinatorial cascading bandits. |
| Open Source Code | No | The paper does not provide a link or explicit statement about releasing the source code for their proposed method or experiments. |
| Open Datasets | Yes | We experiment with the Movie Lens dataset [13] from March 2015. |
| Dataset Splits | No | The paper uses the Movie Lens dataset but does not specify exact training, validation, or test splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, or cloud computing instance types used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or frameworks with their versions) used in the experiments. |
| Experiment Setup | No | While the paper describes the problem settings and general experiment configurations (e.g., network structure, dataset filtering), it does not explicitly provide specific hyperparameters or system-level training settings for the Comb Cascade algorithm (e.g., learning rates, batch sizes, number of epochs, or optimizer settings). |