### 📚 Methods Implemented in **NeuPI**

#### 🔹 *Single-Pass Inference* & *Marginal MAP* in Probabilistic Circuits

**Arya, Shivvrat**, **Rahman, Tahrima**, and **Gogate, Vibhav**.
**"Neural Network Approximators for Marginal MAP in Probabilistic Circuits."**
*Proceedings of the AAAI Conference on Artificial Intelligence*, vol. 38, no. 10, 2024, pp. 10918–10926.
[https://doi.org/10.1609/aaai.v38i10.28966](https://doi.org/10.1609/aaai.v38i10.28966)

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#### 🔹 *Single-Pass Inference* & Neural Embeddings for Constrained MPE

**Arya, Shivvrat**, **Rahman, Tahrima**, and **Gogate, Vibhav**.
**"Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models."**
*Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS)*, PMLR, 2024, pp. 2791–2799.

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#### 🔹 *ITSELF Engine* for General MPE Inference

**Arya, Shivvrat**, **Rahman, Tahrima**, and **Gogate, Vibhav Giridhar**.
**"A Neural Network Approach for Efficiently Answering Most Probable Explanation Queries in Probabilistic Models."**
*Thirty-eighth Conference on Neural Information Processing Systems (NeurIPS)*, 2024.

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#### 🔹 *SINE*: Enhanced Neural Embeddings and Discretization for Probabilistic Graphical Models

**Arya, Shivvrat**, **Rahman, Tahrima**, and **Gogate, Vibhav Giridhar**.
**"SINE: Scalable MPE Inference for Probabilistic Graphical Models Using Advanced Neural Embeddings."**
*Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (AISTATS)*, 2025.