Model Inversion Networks for Model-Based Optimization
Authors: Aviral Kumar, Sergey Levine
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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 aviralk@berkeley.edu |
| 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. |