184 citations · 971 across the 87 of their papers we have counts for
7 papers · 1 filter
Efficient Reinforcement Learning Through Trajectory Generation
Wenqi Cui, Linbin Huang, Weiwei Yang +1
A key barrier to using reinforcement learning (RL) in many real-world applications is the requirement of a large number of system interactions to learn a good control policy. Off-p…
Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks
Liyuan Zheng, Yuanyuan Shi, Lillian J. Ratliff +1
This paper focuses on finding reinforcement learning policies for control systems with hard state and action constraints. Despite its success in many domains, reinforcement learnin…
Transfer Learning for HVAC System Fault Detection
Chase P. Dowling, Baosen Zhang
Faults in HVAC systems degrade thermal comfort and energy efficiency in buildings and have received significant attention from the research community, with data driven methods gain…
Model-Free Renewable Scenario Generation Using Generative Adversarial Networks
Yize Chen, Yishen Wang, Daniel Kirschen +1
Scenario generation is an important step in the operation and planning of power systems with high renewable penetrations. In this work, we proposed a data-driven approach for scena…
Blocking Transferability of Adversarial Examples in Black-Box Learning Systems
Hossein Hosseini, Yize Chen, Sreeram Kannan +2
Advances in Machine Learning (ML) have led to its adoption as an integral component in many applications, including banking, medical diagnosis, and driverless cars. To further broa…
Deceiving Google's Perspective API Built for Detecting Toxic Comments
Hossein Hosseini, Sreeram Kannan, Baosen Zhang +1
Social media platforms provide an environment where people can freely engage in discussions. Unfortunately, they also enable several problems, such as online harassment. Recently,…
Learning Temporal Dependence from Time-Series Data with Latent Variables
Hossein Hosseini, Sreeram Kannan, Baosen Zhang +1
We consider the setting where a collection of time series, modeled as random processes, evolve in a causal manner, and one is interested in learning the graph governing the relatio…