Search Results for author:
Found 7 papers, 2 papers with code
Gen-AI for User Safety: A Survey
However, existing ML/DM classifiers are limited in their ability to understand natural languages w. r. t the context and nuances.
Opportunities and Challenges of Generative-AI in Finance
Gen-AI techniques are able to improve understanding of context and nuances in language modeling, translation between languages, handle large volumes of data, provide fast, low-latency responses and can be fine-tuned for various tasks and domains.
Regression via Implicit Models and Optimal Transport Cost Minimization
This paper addresses the classic problem of regression, which involves the inductive learning of a map, $y=f(x, z)$, $z$ denoting noise, $f:\mathbb{R}^n\times \mathbb{R}^k \rightarrow \mathbb{R}^m$.
Targeted display advertising: the case of preferential attachment
We present an experimental analysis on the historical logs of a major display advertising platform (https://www. criteo. com/).
Benchmarking Regression Methods: A comparison with CGAN
Such a real world situation is best represented using an implicit model in which an extra noise vector, $z$ is included with $x$ as input.
Mining Electronic Health Records: A Survey
In this manuscript, we provide a structured and comprehensive overview of data mining techniques for modeling EHR data.
Causal Inference in Observational Data
We also evaluated our causal rule mining framework on the Electronic Health Records (EHR) data of a large cohort of patients from Mayo Clinic and showed that the patterns we extracted are sufficiently rich to explain the controversial findings in the medical literature regarding the effect of a class of cholesterol drugs on Type-II Diabetes Mellitus (T2DM).
