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..
Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems
Authors: Aman Sinha, Matthew O'Kelly, Russ Tedrake, John C. Duchi
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the efficacy of our approach on a variety of scenarios, illustrating its usefulness as a tool for rapid sensitivity analysis and model comparison that are essential to developing and testing safety-critical autonomous systems. A major focus of this work is empirical, and accordingly, Section 4 empirically demonstrates the superiority of neural bridge sampling over competing techniques in a variety of applications. |
| Researcher Affiliation | Academia | Aman Sinha Stanford University EMAIL Matthew O Kelly University of Pennsylvania EMAIL Russ Tedrake Massachusetts Institute of Technology EMAIL John Duchi Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1 Neural bridge sampling |
| Open Source Code | No | No explicit statement or link found regarding the release of open-source code for the described methodology. |
| Open Datasets | Yes | We evaluate a formally-verified neural network controller [48] on the Open AI Gym continuous Mountain Car environment [67, 17] under a domain perturbation. ... comparing two algorithms on the Open AI Gym Car Racing environment (which requires a surrogate model for gradients) [55]. |
| Dataset Splits | No | The paper discusses simulation environments and probability distributions for defining scenarios, but does not provide specific training/validation/test dataset splits with percentages or sample counts. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) are provided for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'Open AI Gym' and 'masked autoregressive flows (MAFs)' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | All methods are given the same computational budget as measured by evaluations of the simulator. This varies from 50,000-100,000 queries to run Algorithm 1 as determined by pγ (see Appendix C for details of each experiment s hyperparameters). |