Projection-free Online Learning in Dynamic Environments
Authors: Yuanyu Wan, Bo Xue, Lijun Zhang10067-10075
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results validate the efficiency and effectiveness of our algorithm. In this section, we perform numerical experiments in dynamic environments to verify the efficiency and effectiveness of our Multi-OCG+. |
| Researcher Affiliation | Academia | National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China {wanyy, xueb, zhanglj}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 CG, Algorithm 2 OCG+, Algorithm 3 Multi-OCG+ |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | We use a publicly available dataset Movie Lens 100K1, which originally contains 100000 ratings in {1, 2, 3, 4, 5} by 943 users on 1682 movies... 1https://grouplens.org/datasets/movielens/100k/ |
| Dataset Splits | No | The paper mentions dividing the dataset into 'T = 3000 partitions' for online learning but does not specify traditional train/validation/test splits with percentages or sample counts for model training. |
| Hardware Specification | Yes | All algorithms are implemented wtih Matlab R2016b and tested on a linux machine with 2.4GHz CPU and 768GB RAM. |
| Software Dependencies | Yes | All algorithms are implemented wtih Matlab R2016b |
| Experiment Setup | Yes | For our Multi-OCG+, we set H = {γi = 2i|i = 0, ..., log2(T)}. Since ft(X) is not strongly convex, the parameter τ is set to be s/√T, where s is selected from {1e-4, 1e-3, ..., 1.0}. Besides, the parameter ηγ of each expert Eγ is set to be c/√γ, where c is selected from {0.1, 1.0, ..., 1e6}. ... we simply set Kγ = 4 for our Multi-OCG+ to reduce the time cost. |