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..
Improving Multi-Step Prediction of Learned Time Series Models
Authors: Arun Venkatraman, Martial Hebert, J.. Bagnell
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experimental results of our method, DAD, and show significant improvement over the traditional approach in two notably different domains, dynamic system modeling and video texture prediction. |
| Researcher Affiliation | Academia | Arun Venkatraman, Martial Hebert, and J. Andrew Bagnell Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213 EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 DATA AS DEMONSTRATOR (DAD) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | The final dynamical system we test on is the simulated helicopter from (Abbeel and Ng 2005a). |
| Dataset Splits | No | Algorithm 1 mentions 'return Mn with lowest error on validation trajectories' but the experimental setup does not specify the size or split method for validation data. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Random Fourier Feature Regression' and 'Gaussian kernel' but does not provide specific version numbers for any software components. |
| Experiment Setup | Yes | In the experiments described below, we choose m = 500 and use the Gaussian kernel. The hyperparameters λ and kernel bandwidth σ were chosen using cross-validated grid search. |