---
title: "Deep Bucket Elimination"
authors:
  - "Yasaman Razeghi"
  - "Kalev Kask"
  - "Yadong Lu"
  - "Pierre Baldi"
  - "Sakshi Agarwal"
  - "Rina Dechter"
doi: "10.24963/ijcai.2021/582"
url: "https://ijcai.org/proceedings/2021/582"
pdf: "https://ijcai.org/proceedings/2021/0582.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "4235-4242"
track: "Main Track"
keywords:
  - "Uncertainty in AI: Approximate Probabilistic Inference"
  - "Uncertainty in AI: Exact Probabilistic Inference"
---

# Deep Bucket Elimination

Yasaman Razeghi, Kalev Kask, Yadong Lu, Pierre Baldi, Sakshi Agarwal, Rina Dechter

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 4235-4242. DOI: [10.24963/ijcai.2021/582](https://doi.org/10.24963/ijcai.2021/582)

## Abstract

Bucket Elimination (BE) is a universal inference scheme that can solve most tasks over probabilistic and deterministic graphical models exactly.
However, it often requires exponentially high levels of memory (in the induced-width) preventing its execution. In the spirit of exploiting Deep Learning for inference tasks, in this paper, we will use neural networks to approximate BE. 
The resulting Deep Bucket Elimination (DBE) algorithm is developed for computing the partition function.
We provide a proof-of-concept empirically using instances from several different benchmarks, showing that DBE can be a more accurate approximation than current state-of-the-art approaches for approximating  BE (e.g. the mini-bucket schemes), especially when problems are sufficiently hard.