Improved Regret Bounds for Non-Convex Online-Within-Online Meta Learning
Authors: Jiechao Guan, Hui Xiong
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct k-center clustering experiment to verify the convergence performance of task-averaged regret RT,m/m of our Algorithm 1 for non-convex OWO meta learning. |
| Researcher Affiliation | Academia | Jiechao Guan1, Hui Xiong1, 2, 1AI Thrust, The Hong Kong University of Science and Technology (Guangzhou), China 2Guangzhou HKUST Fok Ying Tung Research Institute, China {jiechaoguan, xionghui}@hkust-gz.edu.cn |
| Pseudocode | Yes | Algorithm 1 Non-convex OWO meta learning algorithm for bounded piecewise Lipschitz functions. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | On the one hand, we create a Gaussian mixture binary classification dataset, where each class is a 2-dimensional diagonal Guassian distribution with variance σ and 2σ, as well as the expectation (0, 0) and (bσ, 0). On the other hand, we utilize the split of the real-world Omniglot dataset to create clustering tasks, by drawing random samples each composed of five characters among which four are constant throughout. We set the number T [1, 10] of training tasks and the number m [5, 50] of samples per task for online optimization. Analogous to Balcan et al. (2021), we set the parameters γ = η = 0.01 (not hyper-parameter searched), and set the step size λ in EWA algorithm to minimize the regret in Eq. (2) (not meta-learned). |
| Dataset Splits | No | The paper specifies the number of training tasks and samples per task but does not explicitly provide train/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions software like Python in the appendix but does not provide specific version numbers for any key software components or libraries. |
| Experiment Setup | Yes | Analogous to Balcan et al. (2021), we set the parameters γ = η = 0.01 (not hyper-parameter searched), and set the step size λ in EWA algorithm to minimize the regret in Eq. (2) (not meta-learned). |