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..
Towards Uniformly Superhuman Autonomy via Subdominance Minimization
Authors: Brian Ziebart, Sanjiban Choudhury, Xinyan Yan, Paul Vernaza
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our approach on a computer cursor pointing task, producing behavior that is 78% superhuman, while minimizing demonstration suboptimality provides 50% superhuman behavior and only 72% even after selective data cleaning. |
| Researcher Affiliation | Collaboration | 1Computer Science, University of Illinois Chicago 2Aurora Innovation. Correspondence to: B. Ziebart <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Update w and α from demonstration(s) ξ |
| Open Source Code | No | No statement regarding the availability of open-source code for the methodology is provided in the paper. |
| Open Datasets | No | We focus our experiments on human-generated demonstrations. We analyze pointing task data gathered from 20 non-motor impaired individuals each performing 300 pointing tasks. |
| Dataset Splits | No | We randomly split the dataset into a training set of 200 tasks and a testing set of 100 tasks. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, memory, or cloud instance types) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | We optimize each αk using stochastic exponentiated gradient descent: αk αkeηt(fk( ξ) fk(ξ ) λαk) using an appropriately decaying learning rate ηt, as shown in Algorithm 1. |