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].
Efficient Methods for Non-stationary Online Learning
Authors: Peng Zhao, Yan-Feng Xie, Lijun Zhang, Zhi-Hua Zhou
JMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we provide empirical studies to evaluate our proposed methods. We conduct experiments on the synthetic data. |
| Researcher Affiliation | Academia | Peng Zhao EMAIL Yan-Feng Xie EMAIL Lijun Zhang EMAIL Zhi-Hua Zhou EMAIL National Key Laboratory for Novel Software Technology, Nanjing University, China School of Artiļ¬cial Intelligence, Nanjing University, China |
| Pseudocode | Yes | Algorithm 1 Eļ¬cient Algorithm for Minimizing Dynamic Regret Algorithm 2 Eļ¬cient Algorithm for Problem-dependent Adaptive Regret Algorithm 3 Eļ¬cient Algorithm for Problem-dependent Interval Dynamic Regret Algorithm 4 Eļ¬cient Control Algorithm for Dynamic Policy Regret Algorithm 5 Eļ¬cient Algorithm for Adaptive Regret under PCA Setting |
| Open Source Code | No | No explicit statement about open-source code or a repository link was found in the paper. |
| Open Datasets | No | We conduct experiments on the synthetic data. We consider the following online regression problem. Let T denote the number of total rounds. At each round t [T] the learner outputs the model parameter wt W Rd and simultaneously receives a data sample (xt, yt) with xt X Rd being the feature and yt R being the corresponding label. ... To simulate the non-stationary online environments, we control the way to generate the date samples {(xt, yt)}T t=1. |
| Dataset Splits | No | For dynamic regret minimization, we simulate piecewise-stationary model drifts, as dynamic regret will be linear in T and thus vacuous when the model drift happens every round due to a linear path length measure. Concretely, we split the time horizon evenly into 25 stages and restrict the underlying model parameter w t to be stationary within a stage. For adaptive regret minimization, we simulate gradually evolving model drifts, where the underlying model parameter w t+1 is generated based on the last-round model parameter w t with an additional random walk in the feasible domain W. |
| Hardware Specification | Yes | We use a machine with a single CPU (Intel(R) Core(TM) i9-10900K CPU @ 3.70GHz) and 32GB main memory to conduct the experiments. |
| Software Dependencies | No | In the experiment, we use scipy.optimize.Nonlinear Constraint to solve it to perform the projection onto the feasible domain. (No version number for scipy is provided.) |
| Experiment Setup | Yes | In the simulations, we set T = 20000, the domain diameter as D = 6, and the dimension of the domain as d = 8. The feasible domain W is set as an ellipsoid W = w Rd | w Ew Ī»min(E) (D/2)2 , where E is a certain diagonal matrix and Ī»min(E) denotes its minimum eigenvalue. ... We repeat the experiments for ļ¬ve times with diļ¬erent random seeds and report the results (mean and standard deviation) in Figure 3. ... For dynamic regret minimization, we simulate piecewise-stationary model drifts, as dynamic regret will be linear in T and thus vacuous when the model drift happens every round due to a linear path length measure. Concretely, we split the time horizon evenly into 25 stages and restrict the underlying model parameter w t to be stationary within a stage. For adaptive regret minimization, we simulate gradually evolving model drifts, where the underlying model parameter w t+1 is generated based on the last-round model parameter w t with an additional random walk in the feasible domain W. The step size of random walk is set proportional to D/T to ensure a smooth model change. |