10 citations · 19 across the 7 of their papers we have counts for
8 papers
Towards Balancing Preference and Performance through Adaptive Personalized Explainability
Andrew Silva, Pradyumna Tambwekar, Mariah Schrum +1
As robots and digital assistants are deployed in the real world, these agents must be able to communicate their decision-making criteria to build trust, improve human-robot teaming…
Multi-UAV Planning for Cooperative Wildfire Coverage and Tracking with Quality-of-Service Guarantees
Esmaeil Seraj, Andrew Silva, Matthew Gombolay
In recent years, teams of robot and Unmanned Aerial Vehicles (UAVs) have been commissioned by researchers to enable accurate, online wildfire coverage and tracking. While the major…
FedEmbed: Personalized Private Federated Learning
Andrew Silva, Katherine Metcalf, Nicholas Apostoloff +1
Federated learning enables the deployment of machine learning to problems for which centralized data collection is impractical. Adding differential privacy guarantees bounds on pri…
Learning Interpretable, High-Performing Policies for Autonomous Driving
Rohan Paleja, Yaru Niu, Andrew Silva +3
Gradient-based approaches in reinforcement learning (RL) have achieved tremendous success in learning policies for autonomous vehicles. While the performance of these approaches wa…
Multimodal Punctuation Prediction with Contextual Dropout
Andrew Silva, Barry-John Theobald, Nicholas Apostoloff
Automatic speech recognition (ASR) is widely used in consumer electronics. ASR greatly improves the utility and accessibility of technology, but usually the output is only word seq…
Natural Language Specification of Reinforcement Learning Policies through Differentiable Decision Trees
Pradyumna Tambwekar, Andrew Silva, Nakul Gopalan +1
Human-AI policy specification is a novel procedure we define in which humans can collaboratively warm-start a robot's reinforcement learning policy. This procedure is comprised of…
Cross-Loss Influence Functions to Explain Deep Network Representations
Andrew Silva, Rohit Chopra, Matthew Gombolay
As machine learning is increasingly deployed in the real world, it is paramount that we develop the tools necessary to analyze the decision-making of the models we train and deploy…
Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations
Rohan Paleja, Andrew Silva, Letian Chen +1
Resource scheduling and coordination is an NP-hard optimization requiring an efficient allocation of agents to a set of tasks with upper- and lower bound temporal and resource cons…