mlsanityData to 7 Oct 2026

CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design

arXiv 2302.14015 ↗First posted 27 Feb 2023stat.ML cs.AI cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

We formalize the problem of contextual optimization through the lens of Bayesian experimental design and propose CO-BED -- a general, model-agnostic framework for designing contextual experiments using information-theoretic principles. After formulating a suitable information-based objective, we employ black-box variational methods to simultaneously estimate it and optimize the designs in a single stochastic gradient scheme. In addition, to accommodate discrete actions within our framework, we propose leveraging continuous relaxation schemes, which can naturally be integrated into our variational objective. As a result, CO-BED provides a general and automated solution to a wide range of contextual optimization problems. We illustrate its effectiveness in a number of experiments, where CO-BED demonstrates competitive performance even when compared to bespoke, model-specific alternatives.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 34 of its 77 references that are in the map. First posted on arXiv 27 Feb 2023.

Builds on

34 of 77 references.

References · 34

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  4. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,708
  5. 2018Unsupervised Feature Learning via Non-Parametric Instance-level Discriminationcited by 1,838
  6. 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,135
  7. 2015Automatic differentiation in machine learning: a surveycited by 846
  8. 2019On Variational Bounds of Mutual Informationcited by 550
  9. 2019BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learningcited by 428
  10. 2018Pyro: Deep Universal Probabilistic Programmingcited by 379
  11. 2019Monte Carlo Gradient Estimation in Machine Learningcited by 281
  12. 2017Max-value Entropy Search for Efficient Bayesian Optimizationcited by 202
22 more
  1. 2014Predictive Entropy Search for Efficient Global Optimization of Black-box Functionscited by 201
  2. 2019Variational Bayesian Optimal Experimental Designcited by 116
  3. 2015Batch Bayesian Optimization via Local Penalizationcited by 103
  4. 2021Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Designcited by 103
  5. 2022Deep End-to-end Causal Inferencecited by 93
  6. 2017On Nesting Monte Carlo Estimatorscited by 82
  7. 2016The Parallel Knowledge Gradient Method for Batch Bayesian Optimizationcited by 73
  8. 2020Sequential Batch Learning in Finite-Action Linear Contextual Banditscited by 63
  9. 2021Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoodscited by 61
  10. 2019A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experimentscited by 58
  11. 2020Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimationcited by 56
  12. 2019Nearly Minimax-Optimal Regret for Linearly Parameterized Banditscited by 54
  13. 2020Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Designcited by 51
  14. 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
  15. 2021Variational Causal Networks: Approximate Bayesian Inference over Causal Structurescited by 43
  16. 2021Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Informationcited by 33
  17. 2021Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Boundscited by 26
  18. 2022Model-based Causal Bayesian Optimizationcited by 26
  19. 2020Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?cited by 21
  20. 2021Design of Experiments for Stochastic Contextual Linear Banditscited by 18
  21. 2021Almost Optimal Batch-Regret Tradeoff for Batch Linear Contextual Banditscited by 9
  22. 2020Offline Contextual Bayesian Optimization for Nuclear Fusioncited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 2.

4 papers.

  1. 2024The Essential Role of Causality in Foundation World Models for Embodied AIcited by 32
  2. 2024Amortized Bayesian Experimental Design for Decision-Makingcited by 20
  3. 2026Deep Adaptive Bayesian Screeningcited by 0
  4. 2026Target-Dependent Limits of Causal Repair: A Leading-Log Frontier in a Gaussian Modelcited by 0