Pranjul Yadav
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View article: Gen-AI for User Safety: A Survey
Gen-AI for User Safety: A Survey Open
Machine Learning and data mining techniques (i.e. supervised and unsupervised techniques) are used across domains to detect user safety violations. Examples include classifiers used to detect whether an email is spam or a web-page is reque…
View article: Opportunities and Challenges of Generative-AI in Finance
Opportunities and Challenges of Generative-AI in Finance Open
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 d…
View article: Regression via Implicit Models and Optimal Transport Cost Minimization
Regression via Implicit Models and Optimal Transport Cost Minimization Open
This paper addresses the classic problem of regression, which involves the inductive learning of a map, $y=f(x,z)$, $z$ denoting noise, $f:\mathbb{R}^n\times \mathbb{R}^k \rightarrow \mathbb{R}^m$. Recently, Conditional GAN (CGAN) has been…
View article: Targeted display advertising: the case of preferential attachment
Targeted display advertising: the case of preferential attachment Open
An average adult is exposed to hundreds of digital advertisements daily (https://www.mediadynamicsinc.com/uploads/files/PR092214-Note-only-150-Ads-2mk.pdf), making the digital advertisement industry a classic example of a big-data-driven p…
View article: Frequent Causal Pattern Mining: A Computationally Efficient Framework For Estimating Bias-Corrected Effects
Frequent Causal Pattern Mining: A Computationally Efficient Framework For Estimating Bias-Corrected Effects Open
Our aging population increasingly suffers from multiple chronic diseases simultaneously, necessitating the comprehensive treatment of these conditions. Finding the optimal set of drugs for a combinatorial set of diseases is a combinatorial…
View article: Conditional Generative Adversarial Networks for Regression
Conditional Generative Adversarial Networks for Regression Open
In recent years, impressive progress has been made in the design of implicit probabilistic models via Generative Adversarial Networks (GAN) and its extension, the Conditional GAN (CGAN). Excellent solutions have been demonstrated mostly in…
View article: Benchmarking Regression Methods: A comparison with CGAN
Benchmarking Regression Methods: A comparison with CGAN Open
In recent years, impressive progress has been made in the design of implicit probabilistic models via Generative Adversarial Networks (GAN) and its extension, the Conditional GAN (CGAN). Excellent solutions have been demonstrated mostly in…
View article: Regression with Conditional GAN
Regression with Conditional GAN Open
In recent years, impressive progress has been made in the design of implicit probabilistic models via Generative Adversarial Networks (GAN) and its extension, the Conditional GAN (CGAN). Excellent solutions have been demonstrated mostly in…
View article: Reacting to Variations in Product Demand: An Application for Conversion Rate (CR) Prediction in Sponsored Search
Reacting to Variations in Product Demand: An Application for Conversion Rate (CR) Prediction in Sponsored Search Open
In online internet advertising, machine learning models are widely used to compute the likelihood of a user engaging with product related advertisements. However, the performance of traditional machine learning models is often impacted due…
View article: Reacting to Variations in Product Demand: An Application for Conversion\n Rate (CR) Prediction in Sponsored Search
Reacting to Variations in Product Demand: An Application for Conversion\n Rate (CR) Prediction in Sponsored Search Open
In online internet advertising, machine learning models are widely used to\ncompute the likelihood of a user engaging with product related advertisements.\nHowever, the performance of traditional machine learning models is often\nimpacted …
View article: Mining Electronic Health Records (EHRs)
Mining Electronic Health Records (EHRs) Open
The continuously increasing cost of the US healthcare system has received significant attention. Central to the ideas aimed at curbing this trend is the use of technology in the form of the mandate to implement electronic health records (E…
View article: Estimating Disease Onset Time by Modeling Lab Result Trajectories via Bayes Networks
Estimating Disease Onset Time by Modeling Lab Result Trajectories via Bayes Networks Open
The true onset time of a disease, particularly slow-onset diseases like Type 2 diabetes mellitus (T2DM), is rarely observable in electronic health records (EHRs). However, it is critical for analysis of time to events and for studying sequ…
View article: Causal Pattern Mining in Highly Heterogeneous and Temporal EHRs Data
Causal Pattern Mining in Highly Heterogeneous and Temporal EHRs Data Open
University of Minnesota Ph.D. dissertation. March 2017. Major: Computer Science. Advisor: Vipin Kumar. 1 computer file (PDF); ix, 112 pages.
View article: Mining Electronic Health Records: A Survey
Mining Electronic Health Records: A Survey Open
The continuously increasing cost of the US healthcare system has received significant attention. Central to the ideas aimed at curbing this trend is the use of technology, in the form of the mandate to implement electronic health records (…
View article: Secondary Analysis of an Electronic Surveillance System Combined with Multi-focal Interventions for Early Detection of Sepsis
Secondary Analysis of an Electronic Surveillance System Combined with Multi-focal Interventions for Early Detection of Sepsis Open
Summary Summary: To conduct an independent secondary analysis of a multi-focal intervention for early detection of sepsis that included implementation of change management strategies, electronic surveil-lance for sepsis, and evidence based…
View article: Causal Inference in Observational Data
Causal Inference in Observational Data Open
Our aging population increasingly suffers from multiple chronic diseases simultaneously, necessitating the comprehensive treatment of these conditions. Finding the optimal set of drugs for a combinatorial set of diseases is a combinatorial…
View article: A Data Mining Approach to Determine Sepsis Guideline Impact on Inpatient Mortality and Complications.
A Data Mining Approach to Determine Sepsis Guideline Impact on Inpatient Mortality and Complications. Open
Sepsis incidents have doubled from 2000 through 2008, and hospitalizations for these diagnoses have increased by 70%. The use of the Surviving Sepsis Campaign (SSC) guidelines can lead to earlier diagnosis and treatment; however, the effec…