Zobrazeno 1 - 10
of 536
pro vyhledávání: '"Blei, David"'
One thread of empirical work in social science focuses on decomposing group differences in outcomes into unexplained components and components explained by observable factors. In this paper, we study gender wage decompositions, which require estimati
Externí odkaz:
http://arxiv.org/abs/2409.09894
Autor:
Beltran-Velez, Nicolas, Grande, Alessandro Antonio, Nazaret, Achille, Kucukelbir, Alp, Blei, David
Probabilistic prediction aims to compute predictive distributions rather than single-point predictions. These distributions enable practitioners to quantify uncertainty, compute risk, and detect outliers. However, most probabilistic methods assume pa
Externí odkaz:
http://arxiv.org/abs/2406.07658
Autor:
Jesson, Andrew, Beltran-Velez, Nicolas, Chu, Quentin, Karlekar, Sweta, Kossen, Jannik, Gal, Yarin, Cunningham, John P., Blei, David
This work is about estimating the hallucination rate for in-context learning (ICL) with Generative AI. In ICL, a conditional generative model (CGM) is prompted with a dataset and asked to make a prediction based on that dataset. The Bayesian interpre
Externí odkaz:
http://arxiv.org/abs/2406.07457
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 8, Pp 439-453 (2020)
Topic modeling analyzes documents to learn meaningful patterns of words. However, existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies. To this end, we develop the embedded topic model (etm),
Externí odkaz:
https://doaj.org/article/d7eb59e1b2f64cca9c9d380df8cdc42e
Autor:
Wu, Bohan, Blei, David
Variational inference (VI) has emerged as a popular method for approximate inference for high-dimensional Bayesian models. In this paper, we propose a novel VI method that extends the naive mean field via entropic regularization, referred to as $\Xi$
Externí odkaz:
http://arxiv.org/abs/2404.09113
Autor:
Cai, Diana, Modi, Chirag, Pillaud-Vivien, Loucas, Margossian, Charles C., Gower, Robert M., Blei, David M., Saul, Lawrence K.
Most leading implementations of black-box variational inference (BBVI) are based on optimizing a stochastic evidence lower bound (ELBO). But such approaches to BBVI often converge slowly due to the high variance of their gradient estimates and their
Externí odkaz:
http://arxiv.org/abs/2402.14758
Autor:
Weinstein, Eli N., Blei, David M.
Scientists often want to learn about cause and effect from hierarchical data, collected from subunits nested inside units. Consider students in schools, cells in patients, or cities in states. In such settings, unit-level variables (e.g. each school'
Externí odkaz:
http://arxiv.org/abs/2401.05330
A recent line of work in natural language processing has aimed to combine language models and topic models. These topic-guided language models augment neural language models with topic models, unsupervised learning methods that can discover document-
Externí odkaz:
http://arxiv.org/abs/2312.02331
Inferring causal relationships as directed acyclic graphs (DAGs) is an important but challenging problem. Differentiable Causal Discovery (DCD) is a promising approach to this problem, framing the search as a continuous optimization. But existing DCD
Externí odkaz:
http://arxiv.org/abs/2311.10263
The reliance of text classifiers on spurious correlations can lead to poor generalization at deployment, raising concerns about their use in safety-critical domains such as healthcare. In this work, we propose to use counterfactual data augmentation,
Externí odkaz:
http://arxiv.org/abs/2310.12803