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..
PRASS: Probabilistic Risk-averse Robust Learning with Stochastic Search
Authors: Tianle Zhang, Yanghao Zhang, Ronghui Mu, Jiaxu Liu, Jonathan Fieldsend, Wenjie Ruan
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical experiments demonstrate that PRASS outperforms existing state-of-the-art baselines. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, UK 2Department of Computer Science, University of Exeter, Exeter, EX4 4QF, UK |
| Pseudocode | Yes | The full algorithm is summarised in Algorithm 1 in Appendix C. |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the described methodology. |
| Open Datasets | Yes | We conduct an extensive evaluation of the risk-averse robust learning method on three datasets: MNIST, CIFAR-10 and CIFAR-100. |
| Dataset Splits | No | The paper mentions 'Train/test set evaluations' but does not provide specific percentages or counts for training, validation, and test splits, nor does it explicitly mention a validation split. |
| Hardware Specification | Yes | All the experiments are executed on a system with a 32-Core AMD EPYC 7452 CPU and an NVIDIA A100 40GB GPU. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., PyTorch, TensorFlow, or specific library versions) used for the experiments. |
| Experiment Setup | Yes | For MNIST, we adopt a Re LU network architecture with two convolutional layers, while for CIFAR-10 and CIFAR-100, we utilise an 18-layer residual network architecture. Moreover, the uncertainty set under consideration is a perturbation set, defined as = {δ ∈ Rd : ||δ||∞ ≤ ϵ}, situated within a Gaussian distribution set p(δ) ∈ P. We set ϵ = 0.3 for MNIST and ϵ = 8/255 for CIFAR-10 and CIFAR-100. Full details are provided in Appendix C. |