Conditioning by adaptive sampling for robust design
Authors: David Brookes, Hahnbeom Park, Jennifer Listgarten
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform two main sets of experiments. In the first, we use simple, one-dimensional, toy examples to demonstrate some of the main points about our approach. In the second set of experiments, we ground ourselves in a real protein fluorescence data set, conducting extensive simulations to compare methods. |
| Researcher Affiliation | Academia | 1Biophysics Graduate Group, UC Berkeley, CA 2Department of Biochemistry, University of Washington, Seattle, WA 3Institute for Protein Design, University of Washington, Seattle, WA 4EECS Department, UC Berkeley, CA. |
| Pseudocode | Yes | In Algorithm 1 in the Supplemental Information, we outline our complete procedure when the prior and generative model are both latent variable models of the same parametric form (e. g., both a VAE). |
| Open Source Code | No | The paper does not provide a direct link to a code repository or an explicit statement about releasing the source code for the described methodology. |
| Open Datasets | Yes | We anchored our experiments on a real protein fluorescence data set (Sarkisyan et al., 2016) (see Supplementary Information). |
| Dataset Splits | No | The paper mentions using 5,000 samples for training oracles and refers to a 'hold-out set' and 'test set', but does not provide specific percentages, sample counts, or detailed methodology for train/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions various models and methods but does not provide specific software dependency details, such as library names with version numbers, needed for replication. |
| Experiment Setup | Yes | We use a search distribution that is the same parametric form as the prior, that is, a Gaussian distribution and run our method for 50 iterations, with the quantile update parameter, Q = 1 (meaning that γ(t) will be set to the maximum over the sampled mean oracle values at iteration t) and M = 100 samples taken at each iteration. |