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..
Leveraging Predictions in Smoothed Online Convex Optimization via Gradient-based Algorithms
Authors: Yingying Li, Na Li
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we numerically test the performance of RHIG on quadrotor tracking problems. |
| Researcher Affiliation | Academia | Yingying Li SEAS Harvard University Cambridge, MA, USA. 02138 EMAIL Na Li SEAS Harvard University Cambridge, MA, USA. 02138 EMAIL |
| Pseudocode | Yes | Algorithm 2: Receding Horizon Inexact Gradient (RHIG) |
| Open Source Code | No | The paper does not provide an unambiguous statement of releasing open-source code for the described methodology, nor does it include a direct link to a code repository. |
| Open Datasets | No | The paper describes generating data based on models (e.g., 'quadrotor tracking of a vertically moving target [37]', 'target θt follows: θt = yt + qt, where yt = γyt 1 + et is an autoregressive process with noise et [38]') but does not provide concrete access information (link, DOI, repository, or specific dataset name with access) for a publicly available dataset. |
| Dataset Splits | No | The paper describes numerical experiments but does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or cross-validation setup). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper discusses model parameters for the quadrotor tracking problem (e.g., 'min PT t=1 1 2(α(xt θt)2 + β(xt xt 1)2)', 'γ = 0.3 and γ = 0.7') but does not provide specific experimental setup details such as concrete hyperparameter values (e.g., learning rate, batch size) or training configurations for the RHIG algorithm. |