Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Cai, Hengrui"'
Large language models (LLMs) have shown remarkable performance in various tasks but often fail to handle queries that exceed their knowledge and capabilities, leading to incorrect or fabricated responses. This paper addresses the need for LLMs to rec
Externí odkaz:
http://arxiv.org/abs/2408.05873
Estimating treatment effects from observational data is of central interest across numerous application domains. Individual treatment effect offers the most granular measure of treatment effect on an individual level, and is the most useful to facili
Externí odkaz:
http://arxiv.org/abs/2408.01582
This paper provides robust estimators and efficient inference of causal effects involving multiple interacting mediators. Most existing works either impose a linear model assumption among the mediators or are restricted to handle conditionally indepe
Externí odkaz:
http://arxiv.org/abs/2401.05517
This paper explores the causal reasoning of large language models (LLMs) to enhance their interpretability and reliability in advancing artificial intelligence. Despite the proficiency of LLMs in a range of tasks, their potential for understanding ca
Externí odkaz:
http://arxiv.org/abs/2401.00139
With recent advances in natural language processing, rationalization becomes an essential self-explaining diagram to disentangle the black box by selecting a subset of input texts to account for the major variation in prediction. Yet, existing associ
Externí odkaz:
http://arxiv.org/abs/2306.14115
Autor:
Ma, Tao, Zhu, Jin, Cai, Hengrui, Qi, Zhengling, Chen, Yunxiao, Shi, Chengchun, Laber, Eric B.
In real-world applications of reinforcement learning, it is often challenging to obtain a state representation that is parsimonious and satisfies the Markov property without prior knowledge. Consequently, it is common practice to construct a state la
Externí odkaz:
http://arxiv.org/abs/2303.14281
The causal revolution has stimulated interest in understanding complex relationships in various fields. Most of the existing methods aim to discover causal relationships among all variables within a complex large-scale graph. However, in practice, on
Externí odkaz:
http://arxiv.org/abs/2301.12389
Heterogeneity and comorbidity are two interwoven challenges associated with various healthcare problems that greatly hampered research on developing effective treatment and understanding of the underlying neurobiological mechanism. Very few studies h
Externí odkaz:
http://arxiv.org/abs/2301.12383
In the new era of personalization, learning the heterogeneous treatment effect (HTE) becomes an inevitable trend with numerous applications. Yet, most existing HTE estimation methods focus on independently and identically distributed observations and
Externí odkaz:
http://arxiv.org/abs/2212.14580
In many sports, it is commonly believed that the home team has an advantage over the visiting team, known as the home field advantage. Yet its causal effect on team performance is largely unknown. In this paper, we propose a novel causal inference ap
Externí odkaz:
http://arxiv.org/abs/2205.07193