Research map / Learning theory and algorithms / Matrix and tensor methods
Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| debias PMF with 1bitMC propensity scores | PMF · MovieLoverData | MSE: 0.326 -> 0.299 | Helped |
| debias SVD with 1bitMC propensity scores | SVD · MovieLoverData | MSE: 1.359 -> 0.861 | Helped |
| debias MaxNorm with 1bitMC propensity scores | MaxNorm · MovieLoverData | MSE: 0.674 -> 0.396 | Helped |
| debias SoftImpute with 1bitMC propensity scores | SoftImpute · MovieLoverData | MSE: 0.374 -> 0.412 | Hurt |
| debias SVD with 1bitMC propensity scores | SVD · UserItemData | MSE: 0.139 -> 0.139 | No clear effect |
| debias PMF with 1bitMC propensity scores | PMF · UserItemData | MSE: 0.161 -> 0.146 | Helped |
| debias SoftImpute with 1bitMC propensity scores | SoftImpute · Coat | MSE: 1.064 -> 0.998 | Helped |
| debias SVD with 1bitMC propensity scores | SVD · MovieLens-100k | MSE: 0.862 -> 0.821 | Helped |
| debias MaxNorm with 1bitMC propensity scores | MaxNorm · Coat | MSE: 1.168 -> 1.471 | Hurt |
| estimate propensity with 1bitMC instead of naive Bayes (PMF debiasing) | PMF · MovieLoverData | MSE: 0.363 -> 0.299 | Helped |
| estimate propensities with 1bitMC instead of naive Bayes | propensity estimation · UserItemData | propensity MSE: 0.0150 -> 0.0119 | Helped |
| estimate propensities with 1bitMC instead of naive Bayes | propensity estimation · MovieLoverData | propensity MSE: 0.0346 -> 0.0520 | Hurt |
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