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 [1].
Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization
Authors: Peng Zhao, Yu-Jie Zhang, Lijun Zhang, Zhi-Hua Zhou
JMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section provides empirical studies to validate the effectiveness of our algorithms. ... We compare the performance on both synthetic and real-world datasets. ... We repeat the experiments five times and report the mean and the standard deviation in Figure 1 and Figure 2. |
| Researcher Affiliation | Academia | Peng Zhao EMAIL Yu-Jie Zhang EMAIL Lijun Zhang EMAIL Zhi-Hua Zhou EMAIL National Key Laboratory for Novel Software Technology Nanjing University, Nanjing 210023, China |
| Pseudocode | Yes | Algorithm 1 Sword: meta-algorithm Input: step size pool H; learning rate ε 1: Initialization: i [N], p0,i = 1/N 2: for t = 1 to T do 3: Receive xt+1,i from base-learner Bi 4: Update weight pt+1,i by (16) 5: Predict xt+1 = PN i=1 pt+1,ixt+1,i 6: end for Algorithm 2 Sword: base-algorithm Input: step size ηi H 1: Let bx1,i, x1,i be any point in X 2: for t = 1 to T do 3: bxt+1,i = ΠX bxt,i ηi ft(xt,i) 4: xt+1,i = ΠX bxt+1,i ηi ft(xt,i) 5: Send xt+1,i to the meta-algorithm |
| Open Source Code | Yes | The implementations of all algorithms are based on Py NOL package (Li et al., 2023a). ... Long-Fei Li, Peng Zhao, Yan-Feng Xie, Lijun Zhang, and Zhi-Hua Zhou. Py NOL: : A Python Package for Non-stationary Online Learning, 2023a. URL https://github.com/li-lf/PyNOL. |
| Open Datasets | Yes | Next, we employ a real-world dataset called Sulfur recovery unit (SRU) (Gama et al., 2014; Zhao et al., 2021b), which is a regression dataset with slowly evolving distribution changes. |
| Dataset Splits | No | The paper describes how synthetic data is generated and uses a real-world dataset but does not specify explicit training/test/validation splits for either. It mentions general simulation parameters like T=50000 and S=1000 but no data partitioning methodology for evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory amounts, or specific cloud instances used for running experiments. It generally mentions "gradient computation is the most time-consuming in our simulations" but this is not a hardware specification. |
| Software Dependencies | No | The implementations of all algorithms are based on Py NOL package (Li et al., 2023a). While a software package (PyNOL) is mentioned, a specific version number for this package is not provided, which is required for a reproducible description of software dependencies. |
| Experiment Setup | Yes | Settings. We simulate the online environments as follows. ... In our simulation, we set Γ = 1, D = 2, d = 5, T = 50000, S = 1000, and δ = 2. ... we choose the Huber loss defined as 2(y by)2, for |y by| δ, δ(|y by| 1 2δ), otherwise. |