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..
Matroid Semi-Bandits in Sublinear Time
Authors: Ruo-Chun Tzeng, Naoto Ohsaka, Kaito Ariu
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our technique is based on dynamic maintenance of an approximate maximum-weight basis over inner-product weights. Although the introduction of an approximate maximum-weight basis presents a challenge in regret analysis, we can still guarantee an upper bound on regret as tight as CUCB in the sense that it matches the gap-dependent lower bound by Kveton et al. (2014a) asymptotically. ... For T N, the expected regret of Algorithm 5 is upper bounded by... |
| Researcher Affiliation | Collaboration | Ruo-Chun Tzeng 1 Naoto Ohsaka 2 Kaito Ariu 2 Work done during an internship at Cyber Agent. 1EECS, KTH Royal Institue of Technology, Sweden 2AI Lab, Cyber Agent, Japan. |
| Pseudocode | Yes | Algorithm 1 A greedy maximum-weight basis algorithm ... Algorithm 5 Faster CUCB |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and regret analysis for matroid semi-bandits. It does not conduct experiments on real-world datasets, nor does it provide access to any dataset for training. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments with data. Therefore, it does not provide any details on validation splits. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and analysis. It does not conduct experiments that would require specifying hardware. |
| Software Dependencies | No | The paper is theoretical and does not conduct experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and analysis. It does not conduct empirical experiments, and therefore, no experimental setup details like hyperparameters or training settings are provided. |