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..
Active representation learning for general task space with applications in robotics
Authors: Yifang Chen, Yingbing Huang, Simon S. Du, Kevin G. Jamieson, Guanya Shi
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate different instantiations of our meta algorithm in synthetic datasets and robotics problems, from pendulum simulations to real-world drone flight datasets. On average, our algorithms outperform baselines by 20% 70%. |
| Researcher Affiliation | Academia | 1 Paul G. Allen School of Computer Science & Engineering University of Washington, Seattle,WA EMAIL 2 University of Illinois Urbana-Champaign, Champaign, IL {yh21}@illinois.edu 3 Robotics Institute, Carnegie Mellon University, Pittsburgh, PA {{guanyas }@andrew.cmu.edu |
| Pseudocode | Yes | Algorithm 1 Active multi-task representation learning (general templates) |
| Open Source Code | Yes | 1Code in https://github.com/cloudways X/ALMulti Task_Robotics |
| Open Datasets | Yes | Real-world drone flight dataset [7]. The Neural-Fly dataset [7] includes real flight trajectories using two different drones in various wind conditions. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits (e.g., percentages or exact counts) for any of the datasets used. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, or memory specifications) used for running the experiments were provided in the paper. |
| Software Dependencies | No | The paper mentions using a 'gradient-descent joint training oracle' but does not specify any particular software packages or their version numbers required for reproducibility. |
| Experiment Setup | No | The paper mentions using a 'gradient-descent joint training oracle' but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or other detailed training configurations in the main text. |