mlsanityData to 7 Oct 2026

A* Sampling

arXiv 1411.0030 ↗First posted 31 Oct 2014stat.CO stat.MLcited by 150

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Abstract (arXiv)

The problem of drawing samples from a discrete distribution can be converted into a discrete optimization problem. In this work, we show how sampling from a continuous distribution can be converted into an optimization problem over continuous space. Central to the method is a stochastic process recently described in mathematical statistics that we call the Gumbel process. We present a new construction of the Gumbel process and A* sampling, a practical generic sampling algorithm that searches for the maximum of a Gumbel process using A* search. We analyze the correctness and convergence time of A* sampling and demonstrate empirically that it makes more efficient use of bound and likelihood evaluations than the most closely related adaptive rejection sampling-based algorithms.

Key facts. Cited by 150 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Oct 2014.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 37; 2022: 24; 2023: 23; 2024: 16; 2025: 22; 2026: 27.

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  1. 2021Argmax Flows and Multinomial Diffusion: Learning Categorical Distributionscited by 566
  2. 2021Chasing Sparsity in Vision Transformers: An End-to-End Explorationcited by 181
  3. 2021Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTcited by 104
  4. 2025In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agentscited by 102
  5. 2021A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learningcited by 82
  6. 2021Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognitioncited by 68
  7. 2022Wavelet Score-Based Generative Modelingcited by 61
  8. 2024Informed Correctors for Discrete Diffusion Modelscited by 57
  9. 2023LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation -- Extended Versioncited by 49
  10. 2021Differentiable Subset Pruning of Transformer Headscited by 46
  11. 2023Regularized Vector Quantization for Tokenized Image Synthesiscited by 46
  12. 2023Physically Realizable Natural-Looking Clothing Textures Evade Person Detectors via 3D Modelingcited by 41
38 more
  1. 2022D&D: Learning Human Dynamics from Dynamic Cameracited by 39
  2. 2021Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learningcited by 36
  3. 2023Concrete Subspace Learning based Interference Elimination for Multi-task Model Fusioncited by 36
  4. 2022SIMPLE: A Gradient Estimator for k-Subset Samplingcited by 34
  5. 2024Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvementcited by 32
  6. 2021Dynamic Resolution Networkcited by 31
  7. 2021Algorithms for the Communication of Samplescited by 31
  8. 2023Bridging Discrete and Backpropagation: Straight-Through and Beyondcited by 31
  9. 2023Compression with Bayesian Implicit Neural Representationscited by 25
  10. 2021Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networkscited by 24
  11. 2021Generalizable Human Pose Triangulationcited by 24
  12. 2021Influencing Towards Stable Multi-Agent Interactionscited by 24
  13. 2021Parallel Attention Network with Sequence Matching for Video Groundingcited by 22
  14. 2023Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimationcited by 22
  15. 2023Deep Graph Reprogrammingcited by 22
  16. 2021Classical Planning in Deep Latent Spacecited by 21
  17. 2021Multiresolution Equivariant Graph Variational Autoencodercited by 20
  18. 2021Counterfactual Temporal Point Processescited by 20
  19. 2024Multimodal Action Quality Assessmentcited by 20
  20. 2021Linear, or Non-Linear, That is the Question!cited by 19
  21. 2021Lifelong Teacher-Student Network Learningcited by 18
  22. 2021A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequencescited by 17
  23. 2021Learning Generalized Gumbel-max Causal Mechanismscited by 16
  24. 2023Sparse Backpropagation for MoE Trainingcited by 16
  25. 2018MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generationcited by 15
  26. 2023Understanding and Bridging the Modality Gap for Speech Translationcited by 15
  27. 2023Exponential Smoothing for Off-Policy Learningcited by 15
  28. 2022Fast-R2D2: A Pretrained Recursive Neural Network based on Pruned CKY for Grammar Induction and Text Representationcited by 14
  29. 2022Counterfactual Inference of Second Opinionscited by 14
  30. 2021Modular Meta-Learning for Power Control via Random Edge Graph Neural Networkscited by 13
  31. 2024Gumbel Counterfactual Generation From Language Modelscited by 13
  32. 2025Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancementcited by 13
  33. 2021Localization with Sampling-Argmaxcited by 11
  34. 2022Temporal Multiresolution Graph Neural Networks For Epidemic Predictioncited by 11
  35. 2024Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learningcited by 11
  36. 2021Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Expertscited by 10
  37. 2022A2: Efficient Automated Attacker for Boosting Adversarial Trainingcited by 10
  38. 2022Is margin all you need? An extensive empirical study of active learning on tabular datacited by 10

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