Cascading Bandits: Learning to Rank in the Cascade Model
Authors: Branislav Kveton, Csaba Szepesvari, Zheng Wen, Azin Ashkan
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment with our algorithms on several problems. The algorithms perform surprisingly well even when our modeling assumptions are violated. |
| Researcher Affiliation | Collaboration | Branislav Kveton KVETON@ADOBE.COM Adobe Research, San Jose, CA Csaba Szepesv ari SZEPESVA@CS.UALBERTA.CA Department of Computing Science, University of Alberta Zheng Wen ZHENGWEN@YAHOO-INC.COM Yahoo Labs, Sunnyvale, CA Azin Ashkan AZIN.ASHKAN@TECHNICOLOR.COM Technicolor Research, Los Altos, CA |
| Pseudocode | Yes | The pseudocode of both algorithms is in Algorithm 1. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes generating data from the BLB(L, K, p, ) problem class and the DBN model, rather than using a publicly available, named dataset with access information. For example: "We experiment with the class of problems BLB(L, K, p, ) in Section 4.3." and "We generate data from the dynamic Bayesian network (DBN) model of Chapelle & Zhang (2009)" |
| Dataset Splits | No | The paper does not specify traditional training/validation/test dataset splits. It describes experiments for online learning algorithms (bandits) which are evaluated on cumulative regret over a number of steps. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., specific GPU/CPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | We set p = 0.2; and vary L, K, and . ... We run Cascade UCB1 and Cascade KL-UCB for n = 105 steps. ... The attraction probability of item e is (e) = w(e), where w(e) is given in (6). We set = 0.15. The satisfaction probabilities (e) of all items are the same. We experiment with two settings of (e), 1 and 0.7; and with two settings of persistence γ, 1 and 0.7. We run Cascade KL-UCB for n = 105 steps. |