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..
Variational Auto-Regressive Gaussian Processes for Continual Learning
Authors: Sanyam Kapoor, Theofanis Karaletsos, Thang D Bui
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiments Through our experiments, we highlight the qualitative characteristics of the derived generalized learning objective (8), and provide evidence for the competitiveness of VAR-GPs compared to our main baseline Improved Variational Continual Learning (VCL) (Nguyen et al., 2018; Swaroop et al., 2019), among others. A thorough ablation study demonstrates the efficacy of our modeling choices. |
| Researcher Affiliation | Collaboration | 1Center for Data Science, New York University, New York, NY, USA 2Facebook Inc., Menlo Park, CA, USA 3University of Sydney, Sydney, NSW, Australia. |
| Pseudocode | Yes | Algorithm 1 VAR-GP per-task training |
| Open Source Code | Yes | The full reference implementation of VAR-GPs in Py Torch (Paszke et al., 2019) in publicly available at u.perhapsbay.es/vargp-code. |
| Open Datasets | Yes | Split MNIST Following Zenke et al. (2017), we consider the full 10-way classification task at each time step but receive a dataset D(t) of only a subset of MNIST digits in the sequence 0/1, 2/3, 4/5, 6/7, and 8/9. ... Permuted MNIST In this benchmark, we receive a dataset D(t) of MNIST digits at each time step t, such that the pixels undergo an unknown but fixed permutation. |
| Dataset Splits | Yes | We track validation accuracy on a subset of the training set for early stopping. ... 10000 training samples are cumulatively set aside for validation set across all tasks. ... 10000 samples are set aside for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'Yogi optimizer' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | To optimize both variational objectives (5) and (8), we use a mini-batch size of 512 and 3 samples from variational distribution with the Yogi optimizer (Zaheer et al., 2018). ... We allocate 60 inducing points for each task, with a learning rate of 0.003, and β = 10.0. ... We allocate 100 inducing points for each task, with a learning rate of 0.0037, and β = 1.64. |