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..
Minimal Achievable Sufficient Statistic Learning
Authors: Milan Cvitkovic, Günther Koliander
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In a series of experiments, we show that deep networks trained with MASS Learning achieve competitive performance on supervised learning, regularization, and uncertainty quantification benchmarks. |
| Researcher Affiliation | Academia | 1Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California, USA 2Acoustics Research Institute, Austrian Academy of Sciences, Vienna, Austria. |
| Pseudocode | No | The paper does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code to reproduce all experiments is available online.2 https://github.com/mwcvitkovic/ MASS-Learning |
| Open Datasets | Yes | We performed all experiments on the CIFAR-10 dataset (Krizhevsky, 2009) |
| Dataset Splits | No | The paper mentions 'TRAINING SET SIZE' in tables and 'Test-set classification accuracy', but does not explicitly state a validation set size, percentage, or a specific train/validation/test split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for the experiments. |
| Software Dependencies | No | The paper states 'coded all our models in Py Torch (Paszke et al., 2017)' but does not specify a version number for PyTorch or any other software dependency. |
| Experiment Setup | Yes | We use two networks in our experiments. Small MLP is a feedforward network with two fully-connected layers of 400 and 200 hidden units, respectively, both with elu nonlinearities (Clevert et al., 2015). Res Net20 is the 20-layer residual net of He et al. (2015). In all our experiments, the variational distribution qφ(x|y) for each possible output class y is a mixture of multivatiate Gaussian distributions for which we learn the mixture weights, means, and covariance matrices. ... we use a subsampling strategy: we estimate the Jf term using only a 1/|Y | fraction of the datapoints in a minibatch. ... We performed all experiments on the CIFAR-10 dataset (Krizhevsky, 2009), and coded all our models in Py Torch (Paszke et al., 2017). Full details on all experiments is in Supplementary Material 7.7. |