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..
Model Inversion Networks for Model-Based Optimization
Authors: Aviral Kumar, Sergey Levine
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate MINs on highdimensional model-based optimization problems over images, protein designs, and neural network controller parameters, and bandit optimization from logged data. We experimentally demonstrate MINs in a range of settings, showing that they outperform prior methods on high-dimensional input spaces, such as images, neural network parameters, and protein designs, and substantially outperform prior methods on contextual bandit optimization from logged data. |
| Researcher Affiliation | Academia | Aviral Kumar, Sergey Levine Electrical Engineering and Computer Sciences, UC Berkeley EMAIL |
| Pseudocode | Yes | Algorithm 1 Generic Algorithm for MINs; Algorithm 2 Active MINs with Randomized Labeling |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the MINs methodology itself. It references external codebases like PyTorch GAN and Bandit Net. |
| Open Datasets | Yes | We evaluate on two datasets, which are formed by: (1) selecting random labels xi for each context ci; (2) selecting the correct label 49% of the time, which matches the protocol in [15].; MNIST [17] dataset.; IMDB-Wiki faces [26] dataset; We use the trained scoring oracles released by [4]. |
| Dataset Splits | No | The paper mentions 'training examples' and 'test dataset' but does not explicitly provide details about specific training/validation/test splits (e.g., percentages, sample counts, or a clear citation for the split used). |
| Hardware Specification | No | The paper mentions 'compute support from Google, Amazon, and NVIDIA' in the Acknowledgements, but does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'implemented with PyTorch' in Appendix D.1, but does not specify its version number or other software dependencies with specific versions. |
| Experiment Setup | Yes | For training, we use the Adam optimizer with lr = 0.0001. |