Papers by David Blei
An Invariant Learning Characterization of Controlled Text Generation (2023.acl-long)
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| Challenge: | Controlled generation is a problem of creating text that contains stylistic or semantic attributes of interest. |
| Approach: | They propose a distribution shift-based control system that can be used to train a predictor of the desired attribute. |
| Outcome: | The proposed method shows that the most effective predictor should be invariant across multiple text environments. |
Text-Based Ideal Points (2020.acl-main)
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| Challenge: | Ideal point models analyze lawmakers' votes to quantify their political positions, or ideal points. |
| Approach: | They propose an unsupervised probabilistic topic model that analyzes political texts to quantify the political positions of its authors. |
| Outcome: | The proposed model separates lawmakers by party, learns interpretable politicized topics, and infers ideal points close to the classical vote-based ideal points. |
Rationales for Sequential Predictions (2021.emnlp-main)
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| Challenge: | Sequence models produce accurate predictions, but their decision making processes are hard to explain. |
| Approach: | They propose an efficient algorithm to approximate sequential objective by identifying the most faithful rationales. |
| Outcome: | The proposed algorithm is best at optimizing the sequential objective and provides the most faithful rationales. |
Heterogeneous Supervised Topic Models (2022.tacl-1)
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| Challenge: | Researchers in the social sciences are interested in the relationship between text and an outcome of interest. |
| Approach: | They develop a probabilistic approach to text analysis and prediction using a joint model of text and outcomes to find heterogeneous patterns. |
| Outcome: | The proposed model outperforms other methods on eight datasets and consistently outperformed other models. |