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..
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space
Authors: Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that the new s.i.p.-based algorithm learns more predictive representations than strong baselines. Table 1: Test accuracy of minimizing empirical risk on binary classification tasks. Table 2: Test accuracy on mixup classification task based on 10 random runs. Table 3: Test accuracy on multilabel prediction with logic relationship |
| Researcher Affiliation | Academia | 1University of Illinois at Chicago 2University of Waterloo and Vector Institute. Correspondence to: Xinhua Zhang <EMAIL>. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks, nor does it have clearly labeled algorithm sections. |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing code for the work described, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | We experimented with three image datasets: MNIST, USPS, and Fashion MNIST, each containing 10 classes. We conducted experiments on three multilabel datasets where additional information is available about the hierarchy in its class labels (link): Enron (Klimt and Yang, 2004), WIPO (Rousu et al., 2006), Reuters (Lewis et al., 2004). link. Multilabel dataset. https://sites.google. com/site/hrsvmproject/datasets-hier. |
| Dataset Splits | No | The paper specifies training and test set sizes (e.g., '1000 training and 1000 test examples', 'n examples for training and n examples for testing', '100/100, 200/200, 500/500 randomly drawn train/test examples') but does not explicitly mention a separate validation split or how validation was performed to set hyperparameters if any. |
| Hardware Specification | No | The paper mentions support from 'Google Cloud' but does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | To ease the computation of derivative, we resorted to finite difference for all methods, with two pixels for shifting, 10 degrees for rotation, and 0.1 unit for scaling. The λ was generated from a Beta distribution, whose parameter was tuned to optimize the performance. We also varied p in {n, 2n, 4n} when training Embed. each setting was evaluated 10 times with randomly sampled training and test data. |