Combinatorial Cascading Bandits
Authors: Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvari
NeurIPS 2015 | Conference 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 kveton@adobe.com Azin Ashkan Technicolor Research Los Altos, CA azin.ashkan@technicolor.com Zheng Wen Yahoo Labs Sunnyvale, CA zhengwen@yahoo-inc.com Csaba Szepesvari Department of Computing Science University of Alberta szepesva@cs.ualberta.ca |
| 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). |