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..
Importance-aware Co-teaching for Offline Model-based Optimization
Authors: Ye Yuan, Can (Sam) Chen, Zixuan Liu, Willie Neiswanger, Xue (Steve) Liu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | ICT achieves state-of-the-art results across multiple design-bench tasks, achieving the best mean rank of 3.1 and median rank of 2, among 15 methods. Our source code can be found here. 4 Experimental Results |
| Researcher Affiliation | Academia | 1 Mc Gill University, 2 MILA Quebec AI Institute, 3 University of Washington, 4 Stanford University |
| Pseudocode | Yes | A detailed depiction of the entire algorithm can be found in Algorithm 1. |
| Open Source Code | Yes | Our source code can be found here. |
| Open Datasets | Yes | In this study, we conduct experiments on four continuous tasks and three discrete tasks. The continuous tasks include: (a) Superconductor (Super C)[5]... (b) Ant Morphology (Ant)[1, 14]... (c) D Kitty Morphology (D Kitty)[1, 15]... (d) Hopper Controller (Hopper)[1]... Additionally, our discrete tasks include: (e) TF Bind 8 (TF8)[6]... (f) TF Bind 10 (TF10)[6]... (g) NAS [16]... |
| Dataset Splits | No | The paper describes using an 'offline dataset' and selecting designs from it, but it does not specify explicit train/validation/test splits with percentages or sample counts for the original dataset. |
| Hardware Specification | Yes | All experiments are run on a single NVIDIA GeForce RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer [46]' and implicitly deep learning frameworks, but does not specify software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9'). |
| Experiment Setup | Yes | The number of iterations, T, is set to 200 for continuous tasks and 100 for discrete tasks. ... The learning rates are set at 1e 3 and 1e 1 for continuous tasks and discrete tasks, respectively. ... with a learning rate 2e 1 for continuous tasks and 3e 1 for discrete tasks, respectively. |