27 citations · 69 across the 16 of their papers we have counts for
16 papers
Resolution-Aware Retrieval Augmented Zero-Shot Forecasting
Iman Deznabi, Peeyush Kumar, Madalina Fiterau
Zero-shot forecasting aims to predict outcomes for previously unseen conditions without direct historical data, posing a significant challenge for traditional forecasting methods.…
OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models
Kartik Sharma, Peeyush Kumar, Yunqing Li
This paper presents OG-RAG, an Ontology-Grounded Retrieval Augmented Generation method designed to enhance LLM-generated responses by anchoring retrieval processes in domain-specif…
Economic Anthropology in the Era of Generative Artificial Intelligence
Zachary Sheldon, Peeyush Kumar
This paper explores the intersection of economic anthropology and generative artificial intelligence (GenAI). It examines how large language models (LLMs) can simulate human decisi…
Zero-shot Microclimate Prediction with Deep Learning
Iman Deznabi, Peeyush Kumar, Madalina Fiterau
Weather station data is a valuable resource for climate prediction, however, its reliability can be limited in remote locations. To compound the issue, making local predictions oft…
Privacy Preserving Multi-Agent Reinforcement Learning in Supply Chains
Ananta Mukherjee, Peeyush Kumar, Boling Yang +2
This paper addresses privacy concerns in multi-agent reinforcement learning (MARL), specifically within the context of supply chains where individual strategic data must remain con…
Reward Shaping via Diffusion Process in Reinforcement Learning
Peeyush Kumar
Reinforcement Learning (RL) models have continually evolved to navigate the exploration - exploitation trade-off in uncertain Markov Decision Processes (MDPs). In this study, I lev…
Multi-market Energy Optimization with Renewables via Reinforcement Learning
Lucien Werner, Peeyush Kumar
This paper introduces a deep reinforcement learning (RL) framework for optimizing the operations of power plants pairing renewable energy with storage. The objective is to maximize…
Affordable Artificial Intelligence -- Augmenting Farmer Knowledge with AI
Peeyush Kumar, Andrew Nelson, Zerina Kapetanovic +1
Farms produce hundreds of thousands of data points on the ground daily. Farming technique which combines farming practices with the insights uncovered in these data points using AI…
General sum stochastic games with networked information flows
Sarah H. Q. Li, Lillian J. Ratliff, Peeyush Kumar
Inspired by applications such as supply chain management, epidemics, and social networks, we formulate a stochastic game model that addresses three key features common across these…
Complete Scanning Application Using OpenCv
Ayushe Gangal, Peeyush Kumar, Sunita Kumari
In the following paper, we have combined the various basic functionalities provided by the NumPy library and OpenCv library, which is an open source for Computer Vision application…
Neural Computing
Ayushe Gangal, Peeyush Kumar, Sunita Kumari +1
This chapter aims to provide next-level understanding of the problems of the world and the solutions available to those problems, which lie very well within the domain of neural co…
WisdomNet: Prognosis of COVID-19 with Slender Prospect of False Negative Cases and Vaticinating the Probability of Maturation to ARDS using Posteroanterior Chest X-Rays
Peeyush Kumar, Ayushe Gangal, Sunita Kumari
Coronavirus is a large virus family consisting of diverse viruses, some of which disseminate among mammals and others cause sickness among humans. COVID-19 is highly contagious and…
Recombinant Sort: N-Dimensional Cartesian Spaced Algorithm Designed from Synergetic Combination of Hashing, Bucket, Counting and Radix Sort
Peeyush Kumar, Ayushe Gangal, Sunita Kumari +1
Sorting is an essential operation which is widely used and is fundamental to some very basic day to day utilities like searches, databases, social networks and much more. Optimizin…
Near Optimal Hamiltonian-Control and Learning via Chattering
Peeyush Kumar, Wolf Kohn, Zelda B. Zabinsky
Many applications require solving non-linear control problems that are classically not well behaved. This paper develops a simple and efficient chattering algorithm that learns nea…
Multi-Timescale, Gradient Descent, Temporal Difference Learning with Linear Options
Peeyush Kumar, Doina Precup
Deliberating on large or continuous state spaces have been long standing challenges in reinforcement learning. Temporal Abstraction have somewhat made this possible, but efficientl…
Option Discovery in Hierarchical Reinforcement Learning using Spatio-Temporal Clustering
Aravind Srinivas, Ramnandan Krishnamurthy, Peeyush Kumar +1
This paper introduces an automated skill acquisition framework in reinforcement learning which involves identifying a hierarchical description of the given task in terms of abstrac…