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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank
Authors: Mouxiang Chen, Chenghao Liu, Zemin Liu, Zhuo Li, Jianling Sun
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments In this section, we describe our experimental setup and show the empirical results, in both the fully synthetic setting and large-scale study. |
| Researcher Affiliation | Collaboration | Mouxiang Chen 1 Chenghao Liu 2 Zemin Liu 3 Zhuo Li 4 Jianling Sun 1 1Zhejiang University 2Salesforce Research Asia 3National University of Singapore 4State Street Technology (Zhejiang) Ltd. |
| Pseudocode | Yes | Based on Theorem 1, we illustrate the identifiability check in Algorithm 1. |
| Open Source Code | Yes | Code is available at https://github.com/Keytoyze/ULTR-identifiability |
| Open Datasets | Yes | The datasets can be downloaded from https://webscope.sandbox.yahoo.com/ (Yahoo!), http://quickrank.isti.cnr.it/istella-dataset/ (Istella-S) and http://www.thuir.cn/tiangong-st/ (Tian Gong-ST). |
| Dataset Splits | Yes | We followed the given data split of training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using “Light GBM” and the “ULTRA framework” but does not provide specific version numbers for these or other key software components used in the experiments. |
| Experiment Setup | Yes | The total number of trees was 500, the learning rate was 0.1, number of leaves for one tree was 255. |