Zobrazeno 1 - 10
of 416
pro vyhledávání: '"P P Ghuge"'
Motivated by applications where impatience is pervasive and service times are uncertain, we study a scheduling model where jobs may depart at an unknown point in time and service times are stochastic. Initially, we have access to a single server and
Externí odkaz:
http://arxiv.org/abs/2406.15691
Autor:
Raza, Shaina, Bamgbose, Oluwanifemi, Ghuge, Shardul, Tavakol, Fatemeh, Reji, Deepak John, Bashir, Syed Raza
Large Language Models (LLMs) have advanced various Natural Language Processing (NLP) tasks, such as text generation and translation, among others. However, these models often generate text that can perpetuate biases. Existing approaches to mitigate t
Externí odkaz:
http://arxiv.org/abs/2404.01399
The rapid evolution of Large Language Models (LLMs) highlights the necessity for ethical considerations and data integrity in AI development, particularly emphasizing the role of FAIR (Findable, Accessible, Interoperable, Reusable) data principles. W
Externí odkaz:
http://arxiv.org/abs/2401.11033
Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables. Exact or approximation algorithms have been obtained for several fundamental problems in this area
Externí odkaz:
http://arxiv.org/abs/2312.15427
Despite increasing awareness and research around fake news, there is still a significant need for datasets that specifically target racial slurs and biases within North American political speeches. This is particulary important in the context of upco
Externí odkaz:
http://arxiv.org/abs/2312.03750
We consider the informative path planning ($\mathtt{IPP}$) problem in which a robot interacts with an uncertain environment and gathers information by visiting locations. The goal is to minimize its expected travel cost to cover a given submodular fu
Externí odkaz:
http://arxiv.org/abs/2311.12698
Autor:
Raza, Shaina, Bamgbose, Oluwanifemi, Chatrath, Veronica, Ghuge, Shardul, Sidyakin, Yan, Muaad, Abdullah Y
Bias detection in text is crucial for combating the spread of negative stereotypes, misinformation, and biased decision-making. Traditional language models frequently face challenges in generalizing beyond their training data and are typically design
Externí odkaz:
http://arxiv.org/abs/2310.00347
Publikováno v:
Journal of Clinical and Diagnostic Research, Vol 18, Iss 08, Pp 01-05 (2024)
This article summarises efforts towards understanding the calculation of sample size determination in Randomised controlled Clinical Trials (RCTs). Readers would gain insight into the procedures behind sample size calculation in RCTs and its signific
Externí odkaz:
https://doaj.org/article/9714dfbc0a774a07b6882809f73969a4
We study the $K$-armed dueling bandit problem, a variation of the traditional multi-armed bandit problem in which feedback is obtained in the form of pairwise comparisons. Previous learning algorithms have focused on the $\textit{fully adaptive}$ set
Externí odkaz:
http://arxiv.org/abs/2209.12108
The $K$-armed dueling bandit problem, where the feedback is in the form of noisy pairwise comparisons, has been widely studied. Previous works have only focused on the sequential setting where the policy adapts after every comparison. However, in man
Externí odkaz:
http://arxiv.org/abs/2202.10660