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..
Robust Optimization for Non-Convex Objectives
Authors: Robert S. Chen, Brendan Lucier, Yaron Singer, Vasilis Syrgkanis
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach experimentally on corrupted character classification and robust influence maximization in networks. |
| Researcher Affiliation | Collaboration | Robert Chen Computer Science Harvard University Brendan Lucier Microsoft Research New England Yaron Singer Computer Science Harvard University Vasilis Syrgkanis Microsoft Research New England |
| Pseudocode | Yes | Algorithm 1 Oracle Efficient Improper Robust Optimization; Algorithm 2 Greedy stochastic Oracle for Submodular Maximization Mgreedy |
| Open Source Code | Yes | Code used to implement the algorithms and run the experiments is available at https://github.com/ 12degrees/Robust-Classification/. |
| Open Datasets | Yes | We use the MNIST handwritten digits data set containing 55000 training images, 5000 validation images, and 10000 test images... The Wikipedia Vote Graph [14] |
| Dataset Splits | Yes | We use the MNIST handwritten digits data set containing 55000 training images, 5000 validation images, and 10000 test images |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU, GPU models, or memory specifications used for the experiments. |
| Software Dependencies | No | The paper mentions general software components like "stochastic gradient descent" and "neural network" but does not specify versions for programming languages, libraries, or frameworks (e.g., Python, TensorFlow, PyTorch). |
| Experiment Setup | Yes | The network is trained using Gradient Descent with learning parameter 0.5 through 500 iterations of mini-batches of size 100. In our experiments, we consider four types of corruption (m = 4). In Experiment A, the parameters are |V | = 7115, |E| = 103689, m = 10, p = 0.01 and k = 10. |