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..
Near optimal finite time identification of arbitrary linear dynamical systems
Authors: Tuhin Sarkar, Alexander Rakhlin
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We derive ๏ฌnite time error bounds for estimating general linear time-invariant (LTI) systems from a single observed trajectory using the method of least squares. We provide the ๏ฌrst analysis of the general case when eigenvalues of the LTI system are arbitrarily distributed in three regimes: stable, marginally stable, and explosive. Our analysis yields sharp upper bounds for each of these cases separately. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering and Computer Sciences, MIT 2Department of Brain and Cognitive Sciences, MIT. Correspondence to: Tuhin Sarkar <EMAIL>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, nor does it state that code is released or available. |
| Open Datasets | No | The paper presents theoretical analysis and does not involve empirical studies with specific datasets for training or evaluation. Thus, there is no information regarding public or open datasets. |
| Dataset Splits | No | The paper focuses on theoretical derivations and does not describe empirical experiments, therefore no dataset split information (training, validation, test) is provided. |
| Hardware Specification | No | The paper is theoretical and focuses on mathematical derivations and proofs. It does not describe any empirical experiments, and therefore no specific hardware specifications are mentioned. |
| Software Dependencies | No | The paper is a theoretical work and does not describe empirical experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments, therefore no specific experimental setup details, hyperparameters, or training configurations are provided. |