Prediction with Incomplete Data under Agnostic Mask Distribution Shift

Authors: Yichen Zhu, Jian Yuan, Bo Jiang, Tao Lin, Haiming Jin, Xinbing Wang, Chenghu Zhou

IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Extensive experiments on both synthetic and real-world datasets show that Stable Miss is robust and outperforms state-of-the-art methods under agnostic mask distribution shift.
Researcher Affiliation Academia Yichen Zhu1 , Jian Yuan1 , Bo Jiang 1 , Tao Lin2 , Haiming Jin1 , Xinbing Wang1 and Chenghu Zhou1 1Shanghai Jiao Tong University 2Communication University of China {zyc ieee, yuanjian, bjiang}@sjtu.edu.cn, lintao@cuc.edu.cn, {jinhaiming, xwang8}@sjtu.edu.cn, zhouchsjtu@gmail.com
Pseudocode No No explicitly labeled pseudocode or algorithm blocks are present in the paper.
Open Source Code No The paper does not provide an explicit statement or link for open-source code for the described methodology.
Open Datasets Yes House Sales. Following Shen et al. [2020], we use dataset of house sales in King County, USA... MNIST [Lecun et al., 1998]. The MNIST dataset of handwritten digit images. Traffic [Di Di Chuxing, 2018]. Average traffic speed within every hour from 1343 roads in the city of Chengdu, China, in 2018.
Dataset Splits No The paper does not explicitly state specific training, validation, and test dataset splits (e.g., percentages or sample counts) for reproducibility.
Hardware Specification No The paper does not provide specific details on the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications).
Software Dependencies No The paper does not provide specific software dependencies with version numbers for reproducibility.
Experiment Setup No The paper refers to 'implementation details' being in an extended version ('See the extended version for implementation details.'), but the main text does not specify hyperparameters or other concrete experimental setup details.