mlsanityData to 7 Oct 2026

Unifying Likelihood-free Inference with Black-box Optimization and Beyond

arXiv 2110.03372 ↗First posted 6 Oct 2021cs.LG cs.AI q-bio.BMcited by 10

Not on the map. Why?

Abstract (arXiv)

Black-box optimization formulations for biological sequence design have drawn recent attention due to their promising potential impact on the pharmaceutical industry. In this work, we propose to unify two seemingly distinct worlds: likelihood-free inference and black-box optimization, under one probabilistic framework. In tandem, we provide a recipe for constructing various sequence design methods based on this framework. We show how previous optimization approaches can be "reinvented" in our framework, and further propose new probabilistic black-box optimization algorithms. Extensive experiments on sequence design application illustrate the benefits of the proposed methodology.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 31 of its 74 references that are in the map. First posted on arXiv 6 Oct 2021.

Builds on

31 of 74 references.

References · 31

  1. 2018Regularized Evolution for Image Classifier Architecture Searchcited by 902
  2. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 762
  3. 2017Masked Autoregressive Flow for Density Estimationcited by 680
  4. 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 555
  5. 2020GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generationcited by 301
  6. 2019Evaluating Protein Transfer Learning with TAPEcited by 229
  7. 2018Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flowscited by 217
  8. 2019Automatic Posterior Transformation for Likelihood-Free Inferencecited by 216
  9. 2018Optimization of Molecules via Deep Reinforcement Learningcited by 199
  10. 2017Flexible statistical inference for mechanistic models of neural dynamicscited by 162
  11. 2020ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computingcited by 162
  12. 2017Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Modelscited by 116
19 more
  1. 2019Conditioning by adaptive sampling for robust designcited by 110
  2. 2016Likelihood-free inference by ratio estimationcited by 80
  3. 2020Neural Approximate Sufficient Statistics for Implicit Modelscited by 72
  4. 2019Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Spacecited by 66
  5. 2018Likelihood-free inference with emulator networkscited by 61
  6. 2018Mining gold from implicit models to improve likelihood-free inferencecited by 60
  7. 2020Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learningcited by 57
  8. 2020Guiding Deep Molecular Optimization with Genetic Explorationcited by 51
  9. 2016Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimationcited by 50
  10. 2018Design by adaptive samplingcited by 31
  11. 2018A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networkscited by 30
  12. 2020Population-Based Black-Box Optimization for Biological Sequence Designcited by 29
  13. 2014High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood Computationcited by 19
  14. 2014Likelihood-free inference via classificationcited by 13
  15. 2015Variational Bayes with Intractable Likelihoodcited by 12
  16. 2019Adaptive Gaussian Copula ABCcited by 7
  17. 2018Approximating the Likelihood in Approximate Bayesian Computationcited by 5
  18. 2018Derivative free optimization via repeated classificationcited by 3
  19. 2020Error-guided likelihood-free MCMCcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 4; 2024: 1; 2025: 1; 2026: 1.

10 papers.

  1. 2023Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimizationcited by 57
  2. 2024Diffusion Models as Constrained Samplers for Optimization with Unknown Constraintscited by 33
  3. 2023Distributional GFlowNets with Quantile Flowscited by 28
  4. 2022Unifying Generative Models with GFlowNets and Beyondcited by 24
  5. 2022Plug & Play Directed Evolution of Proteins with Gradient-based Discrete MCMCcited by 17
  6. 2023Pre-Training and Fine-Tuning Generative Flow Networkscited by 16
  7. 2022Latent State Marginalization as a Low-cost Approach for Improving Explorationcited by 13
  8. 2023Importance Weighted Expectation-Maximization for Protein Sequence Designcited by 12
  9. 2025ProSpero: Active Learning for Robust Protein Design Beyond Wild-Type Neighborhoodscited by 3
  10. 2026BOAT: Navigating the Sea of In Silico Predictors for Antibody Design via Multi-Objective Bayesian Optimizationcited by 0