46 citations · 97 across the 6 of their papers we have counts for
7 papers
A Robust Utility Learning Framework via Inverse Optimization
Ioannis C. Konstantakopoulos, Lillian J. Ratliff, Ming Jin +2
In many smart infrastructure applications flexibility in achieving sustainability goals can be gained by engaging end-users. However, these users often have heterogeneous preferenc…
Inverse Reinforcement Learning via Deep Gaussian Process
Ming Jin, Andreas Damianou, Pieter Abbeel +1
We propose a new approach to inverse reinforcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward structures wit…
Building-in-Briefcase (BiB)
Kevin Weekly, Ming Jin, Han Zou +3
A building's environment has profound influence on occupant comfort and health. Continuous monitoring of building occupancy and environment is essential to fault detection, intelli…
SoundLoc: Acoustic Method for Indoor Localization without Infrastructure
Ruoxi Jia, Ming Jin, Costas J. Spanos
Identifying locations of occupants is beneficial to energy management in buildings. A key observation in indoor environment is that distinct functional areas are typically controll…
Social Game for Building Energy Efficiency: Utility Learning, Simulation, and Analysis
Ioannis C. Konstantakopoulos, Lillian J. Ratliff, Ming Jin +2
We describe a social game that we designed for encouraging energy efficient behavior amongst building occupants with the aim of reducing overall energy consumption in the building.…
Modeling of End-Use Energy Profile: An Appliance-Data-Driven Stochastic Approach
Zhaoyi Kang, Ming Jin, Costas J. Spanos
In this paper, the modeling of building end-use energy profile is comprehensively investigated. Top-down and Bottom-up approaches are discussed with a focus on the latter for bette…