# E-LDA: Toward Interpretable LDA Topic Models with Strong Guarantees in Logarithmic Parallel Time

arXiv: https://arxiv.org/abs/2506.07747 · HTML: https://mlsanity.com/paper/2506.07747/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 87 references that are in the map.
- First posted on arXiv 6 Jun 2025.

## Builds on (most cited first, 27 of 27)

- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://mlsanity.com/paper/2109.00725/) (2021)
- [Autoencoding Variational Inference For Topic Models](https://mlsanity.com/paper/1703.01488/) (2017)
- [CausaLM: Causal Model Explanation Through Counterfactual Language Models](https://mlsanity.com/paper/2005.13407/) (2020)
- [Lazier Than Lazy Greedy](https://mlsanity.com/paper/1409.7938/) (2014)
- [How to Make Causal Inferences Using Texts](https://mlsanity.com/paper/1802.02163/) (2018)
- [Effective Neural Topic Modeling with Embedding Clustering Regularization](https://mlsanity.com/paper/2306.04217/) (2023)
- [A Causal Lens for Controllable Text Generation](https://mlsanity.com/paper/2201.09119/) (2022)
- [FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model](https://mlsanity.com/paper/2405.17978/) (2024)
- [A network approach to topic models](https://mlsanity.com/paper/1708.01677/) (2017)
- [Towards the TopMost: A Topic Modeling System Toolkit](https://mlsanity.com/paper/2309.06908/) (2023)
- [Scalable Greedy Feature Selection via Weak Submodularity](https://mlsanity.com/paper/1703.02723/) (2017)
- [A Survey on Neural Topic Models: Methods, Applications, and Challenges](https://mlsanity.com/paper/2401.15351/) (2024)
- [The FAST Algorithm for Submodular Maximization](https://mlsanity.com/paper/1907.06173/) (2019)
- [Keyword Assisted Topic Models](https://mlsanity.com/paper/2004.05964/) (2020)
- [Inference for Regression with Variables Generated by AI or Machine Learning](https://mlsanity.com/paper/2402.15585/) (2024)
- [Submodular Maximization with Matroid and Packing Constraints in Parallel](https://mlsanity.com/paper/1808.09987/) (2018)
- [Leveraging Sparsity for Efficient Submodular Data Summarization](https://mlsanity.com/paper/1703.02690/) (2017)
- [Submodular Function Maximization in Parallel via the Multilinear Relaxation](https://mlsanity.com/paper/1807.08678/) (2018)
- [An Optimal Approximation for Submodular Maximization under a Matroid Constraint in the Adaptive Complexity Model](https://mlsanity.com/paper/1811.03093/) (2018)
- [A Memoization Framework for Scaling Submodular Optimization to Large Scale Problems](https://mlsanity.com/paper/1902.10176/) (2019)
- [Causal Inference on Outcomes Learned from Text](https://mlsanity.com/paper/2503.00725/) (2025)
- [Graph-Sparse LDA: A Topic Model with Structured Sparsity](https://mlsanity.com/paper/1410.4510/) (2014)
- [Provable Algorithms for Inference in Topic Models](https://mlsanity.com/paper/1605.08491/) (2016)
- [Recent Advances in Text Analysis](https://mlsanity.com/paper/2401.00775/) (2024)
- [On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness](https://mlsanity.com/paper/2002.04063/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Manifold-Aware Topic Modeling Approach via Rank-Based Prototypes](https://mlsanity.com/paper/2609.29630/) (2026)

## Abstract (arXiv)

In this paper, we provide the first practical algorithms with provable guarantees for the problem of inferring the topics assigned to each document in an LDA topic model. This is the primary inference problem for many applications of topic models in social science, data exploration, and causal inference settings. We obtain this result by showing a novel non-gradient-based, combinatorial approach to estimating topic models. This yields algorithms that converge to near-optimal posterior probability in logarithmic parallel computation time (adaptivity) -- exponentially faster than any known LDA algorithm. We also show that our approach can provide interpretability guarantees such that each learned topic is formally associated with a known keyword. Finally, we show that unlike alternatives, our approach can maintain the independence assumptions necessary to use the learned topic model for downstream causal inference methods that allow researchers to study topics as treatments. In terms of practical performance, our approach consistently returns solutions of higher semantic quality than solutions from state-of-the-art LDA algorithms, neural topic models, and LLM-based topic models across a diverse range of text datasets and evaluation parameters.