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..
Replicable Online Learning
Authors: Saba Ahmadi, Siddharth Bhandari, Avrim Blum
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our paper is theoretical and we provide proofs for all our theorems. No experiments, just theoretical results. |
| Researcher Affiliation | Academia | Saba Ahmadi Toyota Technological Institute at Chicago EMAIL & Siddharth Bhandari Toyota Technological Institute at Chicago EMAIL & Avrim Blum Toyota Technological Institute at Chicago EMAIL |
| Pseudocode | Yes | Algorithm 1 Follow the Lazy Leader with Block Updates (FLLB(ε, B)) Input: Sequence S = {c1, , c T } arriving one by one over the time. ε > 0 and block size B. 1 Sample p [0, 1 ε)n uniformly at random. 2 Let a1 be a random action picked from A. 3 Let G {p + 1 εz | z Zn}. 4 for t : 1, , T do 5 if t 1 is a multiple of B then 6 gt 1 G c1:t 1 + [0, 1 7 at arg mina A a gt 1. // Stay with the previous action. 8 at at 1. 9 end for |
| Open Source Code | No | No experiments only theoretical results. The answer NA means that paper does not include experiments requiring code. |
| Open Datasets | No | No experiments, just theoretical results. |
| Dataset Splits | No | No experiments, only theoretical results. |
| Hardware Specification | No | No experiments, only theoretical results. |
| Software Dependencies | No | No experiments, only theoretical results. |
| Experiment Setup | No | No experiments, only theoretical results. |