Robust Optimization for Non-Convex Objectives
Authors: Robert S. Chen, Brendan Lucier, Yaron Singer, Vasilis Syrgkanis
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | 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. |