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..
Federated Online and Bandit Convex Optimization
Authors: Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha, Nathan Srebro
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our work is the first attempt towards a systematic understanding of federated online optimization with limited feedback, and it attains tight regret bounds in the intermittent communication setting for both first and zeroth-order feedback. Our results thus bridge the gap between stochastic and adaptive settings in federated online optimization. |
| Researcher Affiliation | Collaboration | Kumar Kshitij Patel 1 Lingxiao Wang 1 Aadirupa Saha 2 Nati Srebro 1 [...] 1TTIC 2Apple. |
| Pseudocode | Yes | Algorithm 1: Non-collaborative OGD (η) [...] Algorithm 2: FEDPOSGD (η, δ) with one-point bandit feedback [...] Algorithm 3: FEDOSGD (η, δ) with two-point bandit feedback |
| Open Source Code | No | The paper does not contain any statement about releasing open-source code for the described methods, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets. It discusses 'function classes' such as FG,B and FH,B, which are mathematical definitions, not actual datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments or hardware specifications used for running them. |
| Software Dependencies | No | The paper is theoretical and does not mention any software implementations or dependencies with version numbers. |
| Experiment Setup | No | The paper describes theoretical choices for algorithm parameters (e.g., 'If we choose η = B G Kd1/4 o , and δ = Bd1/4 '), but these are part of the mathematical analysis and proofs, not specific hyperparameter values or training settings for an empirical experimental setup. |