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..
Projection-free Online Learning in Dynamic Environments
Authors: Yuanyu Wan, Bo Xue, Lijun Zhang10067-10075
AAAI 2021 | Venue PDF | 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 EMAIL |
| 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. |