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..
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. |