A Pairwise Pseudo-likelihood Approach for Matrix Completion with Informative Missingness
Authors: Jiangyuan Li, Jiayi Wang, Raymond K. W. Wong, Kwun Chuen Gary Chan
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The efficacy of our method is validated via numerical experiments, positioning it as a robust tool for matrix completion to mitigate data bias. |
| Researcher Affiliation | Academia | Jiangyuan Li* Department of Statistics Texas A&M University College Station, TX 77843 jiangyuanli@tamu.edu Jiayi Wang* Department of Mathematical Sciences University of Texas at Dallas Richardson, TX 75080 jiayi.wang2@utdallas.edu Raymond K. W. Wong Department of Statistics Texas A&M University College Station, TX 77843 raywong@tamu.edu Kwun Chuen Gary Chan Department of Biostatistics University of Washington Seattle, WA 98195 kcgchan@uw.edu |
| Pseudocode | Yes | Algorithm 1 Projected gradient descent Initialize: Initialize A(0) randomly, set learning rate η. for t = 0 to T do K(t) = A(t) η ℓ(A(t)) Q(t) = Sλ(K(t)) A(t + 1) = POCS(Q(t)) end for Algorithm 2 POCS Initialize: Input matrix Q Rm1 m2. t = 0. while Q = Q do Q = Q Q = Q 1 m1m2 Pm1 i=1 Pm2 j=1 Qi,j J Q i,j = 1 | Qi,j| α Qi,j + 1 | Qi,j| > α sign Qi,j α end while |
| Open Source Code | Yes | The code is publicly available on Git Hub2. 2https://github.com/jiangyuan-li/mc-w-pseudolikelihood |
| Open Datasets | Yes | Tobacco Dataset. This dataset is available in Table 11 in [9]... Coat Shopping Dataset. This dataset is available at https://www.cs.cornell.edu/~schnabts/mnar... Yahoo! Webscope Dataset. This dataset is available at https://webscope.sandbox.yahoo.com/catalog.php?datatype= r&did=3... |
| Dataset Splits | Yes | We use the observed entries as training data and equally split the unobserved data as validation and test data. The validation data is used for hyper-parameter tuning in each method. |
| Hardware Specification | No | The paper mentions "advanced computing resources provided by Texas A&M High Performance Research Computing" in the acknowledgements, but does not specify any particular GPU models, CPU types, or other detailed hardware specifications used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers for replication. |
| Experiment Setup | Yes | Since the objective function is convex in the proposed method, we only tune the regularization parameter λ, and fix the number of iterations as T = 100 and step size η = 1.0 in Algorithm 1. |