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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Optimal Rates in Continual Linear Regression via Increasing Regularization
Authors: Ran Levinstein, Amit Attia, Matan Schliserman, Uri Sherman, Daniel Soudry, Tomer Koren, Itay Evron
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 33,831 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,496 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our paper proves that this gap can be narrowed, or even closed, using two frequently used regularization schemes... We prove that, under jointly realizable tasks, specific choices of fixed and increasing regularization strength schedules yield nearly-optimal and optimal rates of O(log k/k) and O(1/k), respectively. (From NeurIPS checklist, question 4: The paper does not include experiments.) |
| Researcher Affiliation | Collaboration | Department of Computer Science, Technion. Blavatnik School of Computer Science and AI, Tel Aviv University. Blavatnik School of Computer Science and AI, Tel Aviv University, and Google Research. Department of Electrical and Computer Engineering, Technion. |
| Pseudocode | Yes | Scheme 1 Regularized continual linear regression Input: Regression tasks {(Xm, ym)}M m=1, task ordering τ, regularization coefficients (λt)k t=1. Initialize w0 = 0d For each iteration t = 1, . . . , k: wt arg minw 1 2 Xτtw yτt 2 + λt 2 w wt 1 2 Output wk |
| Open Source Code | No | The paper does not include experiments requiring code. |
| Open Datasets | No | We study realizable continual linear regression under random task orderings, a common setting for developing continual learning theory. (From NeurIPS checklist, question 4: The paper does not include experiments.) |
| Dataset Splits | No | The paper does not include experiments. |
| Hardware Specification | No | The paper does not include experiments. |
| Software Dependencies | No | The paper does not include experiments. |
| Experiment Setup | No | The paper does not include experiments. |