Online Non-convex Learning in Dynamic Environments
Authors: Zhipan Xu, Lijun Zhang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we discuss the application to online constrained meta-learning and conduct experiments to verify the effectiveness of our methods. |
| Researcher Affiliation | Academia | National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China School of Artificial Intelligence, Nanjing University, Nanjing, China |
| Pseudocode | Yes | Algorithm 1 Follow the Perturbed Leader (FTPL) |
| Open Source Code | Yes | The code and data are included in supplemental material. |
| Open Datasets | Yes | We use the demonstration data given by Huang et al. [2019] and set the total number of tasks T = 200. |
| Dataset Splits | Yes | For each group, we randomly allocate 50% of the classes for training data, 25% for validation data, and 25% for test data. |
| Hardware Specification | Yes | All experiments are executed on a computer with a 2.50 GHz Intel Xeon Platinum 8255C CPU and an RTX 2080Ti GPU. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and a neural network framework, but does not provide specific version numbers for software dependencies like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | We use the Adam optimizer [Kingma and Ba, 2014] with a learning rate of 0.001 for the optimization. |