90 citations · 117 across the 12 of their papers we have counts for
7 papers
Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions
Wenhao Luo, Wen Sun, Ashish Kapoor
Reinforcement Learning (RL) and continuous nonlinear control have been successfully deployed in multiple domains of complicated sequential decision-making tasks. However, given the…
Learning to Gather Information via Imitation
Sanjiban Choudhury, Ashish Kapoor, Gireeja Ranade +1
The budgeted information gathering problem - where a robot with a fixed fuel budget is required to maximize the amount of information gathered from the world - appears in practice…
Probabilistic Safety Programs
Ashish Kapoor, Debadeepta Dey, Shital Shah
Achieving safe control under uncertainty is a key problem that needs to be tackled for enabling real-world autonomous robots and cyber-physical systems. This paper introduces Proba…
No-Regret Replanning under Uncertainty
Wen Sun, Niteesh Sood, Debadeepta Dey +3
This paper explores the problem of path planning under uncertainty. Specifically, we consider online receding horizon based planners that need to operate in a latent environment wh…
Quantum Deep Learning
Nathan Wiebe, Ashish Kapoor, Krysta M. Svore
In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to effi…
On Discarding, Caching, and Recalling Samples in Active Learning
Ashish Kapoor, Eric J. Horvitz
We address challenges of active learning under scarce informational resources in non-stationary environments. In real-world settings, data labeled and integrated into a predictive…