Search Results for author:
Found 8 papers, 3 papers with code
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
Contextualized Machine Learning
Contextualized ML estimates heterogeneous functions by applying deep learning to the meta-relationship between contextual information and context-specific parametric models.
LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs
We show that large language models (LLMs) are remarkably good at working with interpretable models that decompose complex outcomes into univariate graph-represented components.
NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters
We encode the acyclicity constraint as a smooth regularization loss which is back-propagated to the mixing function; in this way, NOTMAD shares information between context-specific acyclic graphs, enabling the estimation of Bayesian network structures and parameters at even single-sample resolution.
Causal Mediation Analysis Leveraging Multiple Types of Summary Statistics Data
We can achieve necessary interpretation of GWAS in a causal mediation framework, looking to establish a sparse set of mediators between genetic and downstream variables, but there are several challenges.
A latent topic model for mining heterogenous non-randomly missing electronic health records data
Electronic health records (EHR) are rich heterogeneous collection of patient health information, whose broad adoption provides great opportunities for systematic health data mining.
Network Maximal Correlation
For jointly Gaussian variables, we show that under some conditions the NMC optimization is an instance of the Max-Cut problem.
Diversifying Sparsity Using Variational Determinantal Point Processes
We propose a novel diverse feature selection method based on determinantal point processes (DPPs).
