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..
Stochastic Optimization for Multiview Representation Learning using Partial Least Squares
Authors: Raman Arora, Poorya Mianjy, Teodor Marinov
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the performance of our methods against other stochastic baselines discussed in Section 2, in terms of the progress made on the objective as a function of the number of iterations as well as the CPU runtime, on both synthetic and real-world datasets. [...] Figure 1 shows the PLS objective as a function of the number of iterations (samples processed) as well as CPU runtime, for target dimensionality k {2, 4, 8}. [...] Figure 2 shows the PLS objective, as a function of the number of samples processed (iterations) as well as CPU runtime, for ranks k {2, 4, 8}. |
| Researcher Affiliation | Academia | Raman Arora EMAIL Poorya Mianjy EMAIL Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218. Teodor V. Marinov EMAIL School of Informatics, University of Edinburgh, Edinburgh UK, EH8 9AB |
| Pseudocode | Yes | Algorithm 1 Matrix Stochastic Gradient [...] Algorithm 2 Matrix Exponentiated Gradient |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | In this section, we discuss experiments on the University of Wisconsin X-ray Microbeam (XRMB) Database (Westbury, 1994). |
| Dataset Splits | Yes | Each view is split into training, tuning and testing sets, each of size n. [...] Because we cannot evaluate the true population objective for Problem 1, we instead approximate them by evaluating on a held-out testing sample (half of the dataset, with the other half being used for training). All results are averaged over 50 random train/test splits. |
| Hardware Specification | No | No specific hardware details (like CPU/GPU models, processor types, or memory amounts) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions, or solver versions) needed to replicate the experiments. |
| Experiment Setup | Yes | We tune the initial learning rate parameter η0 for each algorithm over the set {0.001, 0.01, 0.1, 1, 10}. All algorithms were run for only one pass over the training data. [...] we deliberately set all initial learning rates η0 = 1, choosing ηt = 1/t uniformly for all experiments. |