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..
Logarithmic Regret for Online Control
Authors: Naman Agarwal, Elad Hazan, Karan Singh
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We presented two algorithms for controlling linear dynamical systems with strongly convex costs with regret that scales poly-logarithmically with time. This improves state-of-the-art known regret bounds that scale as O(T). It remains open to extend the poly-log regret guarantees to more general systems and loss functions, such as exp-concave losses, or alternatively, show that this is impossible. |
| Researcher Affiliation | Collaboration | Naman Agarwal1 Elad Hazan1 2 Karan Singh1 2 1 Google AI Princeton 2 Computer Science, Princeton University EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Online Control Algorithm |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper is theoretical and does not use or describe a publicly available dataset for experiments. It mentions 'noise wt is a random variable generated independently at every time step' which is a theoretical assumption. |
| Dataset Splits | No | The paper is theoretical and does not provide specific details regarding dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details, hyperparameters, or training configurations. |