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].
Cascading Bandits: Learning to Rank in the Cascade Model
Authors: Branislav Kveton, Csaba Szepesvari, Zheng Wen, Azin Ashkan
ICML 2015 | Venue PDF | 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 EMAIL Adobe Research, San Jose, CA Csaba Szepesv ari EMAIL Department of Computing Science, University of Alberta Zheng Wen EMAIL Yahoo Labs, Sunnyvale, CA Azin Ashkan EMAIL 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. |