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..
Fast Tensor Completion via Approximate Richardson Iteration
Authors: Mehrdad Ghadiri, Matthew Fahrbach, Yunbum Kook, Ali Jadbabaie
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We analyze the convergence rate of our approximate Richardson iteration-based algorithm, and our empirical study shows that it can be 100x faster than direct methods for CP completion on real-world tensors. |
| Researcher Affiliation | Collaboration | 1MIT 2Google Research 3Georgia Tech. Correspondence to: Mehrdad Ghadiri <EMAIL>. |
| Pseudocode | Yes | Algorithm 1: approx-mini-als |
| Open Source Code | Yes | The code is available at https://github.com/fahrbach/fast-tensor-completion. |
| Open Datasets | Yes | HYPERSPECTRAL is 1024 × 1344 × 33 tensor of time-lapse hyperspectral radiance images (Nascimento et al., 2016). |
| Dataset Splits | Yes | For a given tensor X and sample ratio p ∈ [0, 1], let XΩbe a partially observed tensor with a random p fraction of entries revealed. We fit XΩwith a rank-R CP decomposition by minimizing the training relative reconstruction error (RRE) (b X − X)Ω F/ XΩ F using different ALS algorithms. ...the test RRE b X − X F/ X F (dashed line) is the loss on the entire (mostly unobserved) tensor |
| Hardware Specification | Yes | All experiments are implemented with NumPy (Harris et al., 2020) and Tensorly (Kossaifi et al., 2019) on an Apple M2 chip with 8 GB of RAM. |
| Software Dependencies | Yes | All experiments are implemented with NumPy (Harris et al., 2020) and Tensorly (Kossaifi et al., 2019) on an Apple M2 chip with 8 GB of RAM. |
| Experiment Setup | Yes | For these experiments, we initialize X = Y = I. ...we set p = 0.1 and sweep over the rank R using direct to demonstrate how the train RRE (solid line) and test RRE b X − X F/ X F (dashed line) decrease as a function of R and the ALS step count. In the second column, we fix the parameters (p, R) = (0.1, 16) |