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..
Uncertainty-Aware Instance Reweighting for Off-Policy Learning
Authors: Xiaoying Zhang, Junpu Chen, Hongning Wang, Hong Xie, Yang Liu, John C.S. Lui, Hang Li
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results on the synthetic and real-world recommendation datasets demonstrate that UIPS significantly improves the quality of the discovered policy, when compared against an extensive list of state-of-the-art baselines. |
| Researcher Affiliation | Collaboration | 1Byte Dance Research 2Chong Qing University 3Tsinghua University 4 Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Science 5 The Chinese University of Hong Kong |
| Pseudocode | Yes | Algorithm 1 UIPS (found in Appendix 7.1) |
| Open Source Code | Yes | All data and code can be found in https://github.com/Xiaoyinggit/UIPS.git. |
| Open Datasets | Yes | We evaluate UIPS on both synthetic data and three real-world datasets with unbiased collection... Yahoo! R31; (2) Coat2; (3) Kuai Rec [12]... The Wiki10-31K dataset contains approximately 20K samples. |
| Dataset Splits | Yes | We split the dataset into train, validation and test sets with size 11K:3K:6K. (synthetic data) ... a small part of unbiased data split for validation purpose (5% on Yahoo R3 and Coat, and 15% on Kuai Rec). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using neural networks and logistic regression but does not provide specific software dependencies or their version numbers. |
| Experiment Setup | Yes | the learning rate was searched in {1e 5, 1e 4, 1e 3, 1e 2}; λ, γ, η1 were searched in {0.5, 0.1, 1, 2,5, 10, 15, 20, 25, 30, 40, 50}. And η2 was searched in {1, 10, 100, 1000}. |