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..
Bidirectional Learning for Offline Infinite-width Model-based Optimization
Authors: Can Chen, Yingxueff Zhang, Jie Fu, Xue (Steve) Liu, Mark Coates
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on various tasks verify the effectiveness of BDI. |
| Researcher Affiliation | Collaboration | 1Mc Gill University, 2 Huawei Noah s Ark Lab, 3 Beijing Academy of Artificial Intelligence |
| Pseudocode | No | The paper describes the method using mathematical equations and text but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The code is available here. (In abstract). We include our code in the supplemental material as a new asset and we will release this new asset upon paper acceptance. |
| Open Datasets | Yes | To evaluate the effectiveness of BDI, we adopt the commonly used design-bench, including both continuous and discrete tasks, and the evaluation protocol as in the prior work [8]. |
| Dataset Splits | Yes | We follow the training settings of [8] for all comparison methods if not specified. |
| Hardware Specification | Yes | All Jax experiments are run on multiple CPUs within a cluster and all Pytorch experiments are run on one V100 GPU. |
| Software Dependencies | No | We use the NTK library [29] build on Jax [30] to conduct kernel-based experiments and use Pytorch [31] for other experiments. |
| Experiment Setup | Yes | We set the regularization β as 1e 6 following [16]. We set the predefined target score yh as a constant 10 across all tasks, which proves to be effective and robust in all cases. We set α as 1e 3 for all continuous tasks and as 0.0 for all discrete tasks, and set the number of iterations T to 200 in all experiments. We adopt a 6-layer MLP (Multi Layer Perceptron) followed by Re LU for all gradient updating methods and the hidden size is set as 2048. We optimize L(Xh) with the Adam optimizer [21] with a 1e 1 learning rate for discrete tasks and a 1e 3 learning rate for continuous tasks. |