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..
Sparsity in Partially Controllable Linear Systems
Authors: Yonathan Efroni, Sham Kakade, Akshay Krishnamurthy, Cyril Zhang
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also corroborate these theoretical improvements over certaintyequivalent control through a simulation study. and We present a proof-of-concept empirical study, to demonstrate the end-to-end statistical advantages of leveraging sparsity in the LQR of a PC-LQ. and Figure 1 clearly shows experimental results. |
| Researcher Affiliation | Industry | 1Microsoft Research New York, NY. Correspondence to: Yonathan Efroni <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Learning Optimal Policy of PC-LQ and Algorithm 2 Semiparametric Least Squares |
| Open Source Code | No | The paper does not explicitly state that source code for the methodology is available or provide a link to a code repository. |
| Open Datasets | No | Synthetic PC-LQ problems were generated with i.i.d. standard Gaussian entries (for all A1, A2, A3, A12, A32, B1); The paper uses synthetic data and does not provide concrete access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes generating synthetic data and conducting 100 trials for evaluation, but does not specify train/validation/test dataset splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names like PyTorch with version 1.9, or specific solver versions). |
| Experiment Setup | Yes | Synthetic PC-LQ problems were generated with i.i.d. standard Gaussian entries (for all A1, A2, A3, A12, A32, B1); the diagonal blocks were normalized by their top singular values so that ρ(A1) = 1, and ρ(A2) = ρ(A3) = 0.9. and ...soft-thresholded semiparametric least-squares estimator from Algorithm 1 (with ϵ = 0.1)... |