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20182022
most citedDesign, Benchmarking and Explainability Analysis of a Game-Theoretic Framework towards Energy Efficiency in Smart Infrastructure

9 citations · 27 across the 7 of their papers we have counts for

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cs.LG20226 cited

Machine Learning for Smart and Energy-Efficient Buildings

Hari Prasanna Das, Yu-Wen Lin, Utkarsha Agwan +7

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countr…

cs.LG2022

Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning

Doseok Jang, Larry Yan, Lucas Spangher +1

Multi-Agent Reinforcement Learning currently focuses on implementations where all data and training can be centralized to one machine. But what if local agents are split across mul…

cs.LG20211 cited

Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data

Hari Prasanna Das, Ryan Tran, Japjot Singh +4

At the onset of a pandemic, such as COVID-19, data with proper labeling/attributes corresponding to the new disease might be unavailable or sparse. Machine L…

cs.LG20212 cited

Offline-Online Reinforcement Learning for Energy Pricing in Office Demand Response: Lowering Energy and Data Costs

Doseok Jang, Lucas Spangher, Manan Khattar +3

Our team is proposing to run a full-scale energy demand response experiment in an office building. Although this is an exciting endeavor which will provide value to the community,…

cs.LG20199 cited

Design, Benchmarking and Explainability Analysis of a Game-Theoretic Framework towards Energy Efficiency in Smart Infrastructure

Ioannis C. Konstantakopoulos, Hari Prasanna Das, Andrew R. Barkan +6

In this paper, we propose a gamification approach as a novel framework for smart building infrastructure with the goal of motivating human occupants to reconsider personal energy u…

cs.LG2019

Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms

Ruoxi Jia, David Dao, Boxin Wang +6

Given a data set containing millions of data points and a data consumer who is willing to pay for $ to train a machine learning (ML) model over , how…