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..
On the Convergence of Certified Robust Training with Interval Bound Propagation
Authors: Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we present a theoretical analysis on the convergence of IBP training... We further conduct experiments to compare the convergence of networks with different widths m for natural training and IBP training respectively. |
| Researcher Affiliation | Academia | Yihan Wang*, Zhouxing Shi*, Quanquan Gu, Cho-Jui Hsieh University of California, Los Angeles EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | Yes | We use the MNIST (Le Cun et al., 2010) dataset and take digit images with label 2 and 5 for binary classification. |
| Dataset Splits | No | The paper mentions training the model but does not specify training, validation, or test dataset splits. |
| Hardware Specification | Yes | even if we enlarge m up to 80,000 limited by the memory of a single Ge Force RTX 2080 GPU |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | We train the model for 70 epochs with SGD, and we keep ϵ fixed throughout the whole training process. |