Stochastic Optimization for Multiview Representation Learning using Partial Least Squares
Authors: Raman Arora, Poorya Mianjy, Teodor Marinov
ICML 2016 | Conference PDF | Archive PDF | Plain Text | 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 ARORA@CS.JHU.EDU Poorya Mianjy MIANJY@JHU.EDU Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218. Teodor V. Marinov T.V.MARINOV@SMS.ED.AC.UK 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. |