# Partial Label Learning for Automated Theorem Proving

arXiv: https://arxiv.org/abs/2507.03314 · HTML: https://mlsanity.com/paper/2507.03314/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 36 references that are in the map.
- First posted on arXiv 4 Jul 2025.

## Builds on (most cited first, 19 of 19)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [Thinking Fast and Slow with Deep Learning and Tree Search](https://mlsanity.com/paper/1705.08439/) (2017)
- [Provably Consistent Partial-Label Learning](https://mlsanity.com/paper/2007.08929/) (2020)
- [DeepMath - Deep Sequence Models for Premise Selection](https://mlsanity.com/paper/1606.04442/) (2016)
- [Reinforcement Learning of Theorem Proving](https://mlsanity.com/paper/1805.07563/) (2018)
- [Leveraged Weighted Loss for Partial Label Learning](https://mlsanity.com/paper/2106.05731/) (2021)
- [Graph Representations for Higher-Order Logic and Theorem Proving](https://mlsanity.com/paper/1905.10006/) (2019)
- [Deep Network Guided Proof Search](https://mlsanity.com/paper/1701.06972/) (2017)
- [Partial Label Learning with Self-Guided Retraining](https://mlsanity.com/paper/1902.03045/) (2019)
- [Premise Selection for Theorem Proving by Deep Graph Embedding](https://mlsanity.com/paper/1709.09994/) (2017)
- [From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood](https://mlsanity.com/paper/1704.07926/) (2017)
- [ENIGMA: Efficient Learning-based Inference Guiding Machine](https://mlsanity.com/paper/1701.06532/) (2017)
- [Property Invariant Embedding for Automated Reasoning](https://mlsanity.com/paper/1911.12073/) (2019)
- [ATPboost: Learning Premise Selection in Binary Setting with ATP Feedback](https://mlsanity.com/paper/1802.03375/) (2018)
- [Towards Finding Longer Proofs](https://mlsanity.com/paper/1905.13100/) (2019)
- [The Role of Entropy in Guiding a Connection Prover](https://mlsanity.com/paper/2105.14706/) (2021)
- [MaLeS: A Framework for Automatic Tuning of Automated Theorem Provers](https://mlsanity.com/paper/1308.2116/) (2013)
- [Towards Unbiased Exploration in Partial Label Learning](https://mlsanity.com/paper/2307.00465/) (2023)

## Abstract (arXiv)

We formulate learning guided Automated Theorem Proving as Partial Label Learning, building the first bridge across these fields of research and providing a theoretical framework for dealing with alternative proofs during learning. We use the plCoP theorem prover to demonstrate that methods from the Partial Label Learning literature tend to increase the performance of learning assisted theorem provers.