# Design by adaptive sampling

arXiv: https://arxiv.org/abs/1810.03714 · HTML: https://mlsanity.com/paper/1810.03714/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2018.

## Built on by (most cited first, 30 of 32)

- [Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces](https://mlsanity.com/paper/2111.01186/) (2021)
- [Protein sequence design with deep generative models](https://mlsanity.com/paper/2104.04457/) (2021)
- [Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences](https://mlsanity.com/paper/2306.03111/) (2023)
- [Deep Extrapolation for Attribute-Enhanced Generation](https://mlsanity.com/paper/2107.02968/) (2021)
- [Advancing Bayesian Optimization via Learning Correlated Latent Space](https://mlsanity.com/paper/2310.20258/) (2023)
- [Plug & Play Directed Evolution of Proteins with Gradient-based Discrete MCMC](https://mlsanity.com/paper/2212.09925/) (2022)
- [SurfPro: Functional Protein Design Based on Continuous Surface](https://mlsanity.com/paper/2405.06693/) (2024)
- [Adaptive machine learning for protein engineering](https://mlsanity.com/paper/2106.05466/) (2021)
- [Improved Off-policy Reinforcement Learning in Biological Sequence Design](https://mlsanity.com/paper/2410.04461/) (2024)
- [Importance Weighted Expectation-Maximization for Protein Sequence Design](https://mlsanity.com/paper/2305.00386/) (2023)
- [Unifying Likelihood-free Inference with Black-box Optimization and Beyond](https://mlsanity.com/paper/2110.03372/) (2021)
- [Steering Generative Models with Experimental Data for Protein Fitness Optimization](https://mlsanity.com/paper/2505.15093/) (2025)
- [Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate](https://mlsanity.com/paper/2106.09019/) (2021)
- [Variational Search Distributions](https://mlsanity.com/paper/2409.06142/) (2024)
- [Generative Enzyme Design Guided by Functionally Important Sites and Small-Molecule Substrates](https://mlsanity.com/paper/2405.08205/) (2024)
- [Parallelizing Contextual Bandits](https://mlsanity.com/paper/2105.10590/) (2021)
- [ReLSO: A Transformer-based Model for Latent Space Optimization and Generation of Proteins](https://mlsanity.com/paper/2201.09948/) (2022)
- [Few Shot Protein Generation](https://mlsanity.com/paper/2204.01168/) (2022)
- [Joint Design of Protein Sequence and Structure based on Motifs](https://mlsanity.com/paper/2310.02546/) (2023)
- [Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization](https://mlsanity.com/paper/2401.06173/) (2024)
- [OPT-GAN: A Broad-Spectrum Global Optimizer for Black-box Problems by Learning Distribution](https://mlsanity.com/paper/2102.03888/) (2021)
- [Robust Model-Based Optimization for Challenging Fitness Landscapes](https://mlsanity.com/paper/2305.13650/) (2023)
- [Functional Geometry Guided Protein Sequence and Backbone Structure Co-Design](https://mlsanity.com/paper/2310.04343/) (2023)
- [Reliable algorithm selection for machine learning-guided design](https://mlsanity.com/paper/2503.20767/) (2025)
- [Reinforcement Learning for Sequence Design Leveraging Protein Language Models](https://mlsanity.com/paper/2407.03154/) (2024)
- [Boosting In-Silicon Directed Evolution with Fine-Tuned Protein Language Model and Tree Search](https://mlsanity.com/paper/2511.09900/) (2025)
- [Leveraging Discrete Function Decomposability for Scientific Design](https://mlsanity.com/paper/2511.03032/) (2025)
- [Efficient Protein Optimization via Structure-aware Hamiltonian Dynamics](https://mlsanity.com/paper/2601.11012/) (2026)
- [Active Flow Matching](https://mlsanity.com/paper/2603.00877/) (2026)
- [Self-Improvement Imitation with Biologically Guided Search for Protein Design Under Oracle Budgets](https://mlsanity.com/paper/2605.26690/) (2026)

## Abstract (arXiv)

We present a probabilistic modeling framework and adaptive sampling algorithm wherein unsupervised generative models are combined with black box predictive models to tackle the problem of input design. In input design, one is given one or more stochastic "oracle" predictive functions, each of which maps from the input design space (e.g. DNA sequences or images) to a distribution over a property of interest (e.g. protein fluorescence or image content). Given such stochastic oracles, the problem is to find an input that is expected to maximize one or more properties, or to achieve a specified value of one or more properties, or any combination thereof. We demonstrate experimentally that our approach substantially outperforms other recently presented methods for tackling a specific version of this problem, namely, maximization when the oracle is assumed to be deterministic and unbiased. We also demonstrate that our method can tackle more general versions of the problem.