Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
Authors: Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang
ICML 2021 | Venue PDF | LLM Run Details | Input Tokens: 20,911 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,693 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive validation on standard benchmarks demonstrate that the algorithm, in combination with carefully designed sampling procedures, is significantly more accurate than existing techniques, thus promising to be the first practical DP embedding model. |
| Researcher Affiliation | Industry | 1Google Research. Correspondence to: Walid Krichene <EMAIL>, Abhradeep Thakurta <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 DPALS: Private Matrix Completion via Alternating Minimization |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the availability of its source code. |
| Open Datasets | Yes | Movie Lens data sets. We apply our method to two common recommender benchmarks: (i) rating prediction on Movie Lens 10M (ML-10M) following Lee et al. (2013), where the task is to predict the value of a user s rating, and performance is measured using the RMSE, (ii) item recommendation on Movie Lens 20M (ML-20M) following Liang et al. (2018), where the task is to select k movies for each user and performance is measured using Recall@k. |
| Dataset Splits | No | Each data set is partitioned into training, validation and test sets. Hyper-parameters are chosen on the validation set, and the final performance is measured on the test set. However, the paper does not specify the exact percentages or methodology for these splits. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running experiments (e.g., GPU models, CPU types). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Additional details on the experimental setup are in Appendix D, including statistics of the data sets, a list of hyper-parameters and the ranges we used for each. |