5 papers
Disentangled and Distilled Encoder for Out-of-Distribution Reasoning with Rademacher Guarantees
Zahra Rahiminasab, Michael Yuhas, Arvind Easwaran
Recently, the disentangled latent space of a variational autoencoder (VAE) has been used to reason about multi-label out-of-distribution (OOD) test samples that are derived from di…
Managing Charging Induced Grid Stress and Battery Degradation in Electric Taxi Fleets
Michael Yuhas, Rajesh K. Ahir, Laksamana Vixell Tanjaya Hartono +3
Operating fleets of electric vehicles (EVs) introduces several challenges, some of which are borne by the fleet operator, and some of which are borne by the power grid. To maximize…
CRLLK: Constrained Reinforcement Learning for Lane Keeping in Autonomous Driving
Xinwei Gao, Arambam James Singh, Gangadhar Royyuru +2
Lane keeping in autonomous driving systems requires scenario-specific weight tuning for different objectives. We formulate lane-keeping as a constrained reinforcement learning prob…
Guaranteeing Out-Of-Distribution Detection in Deep RL via Transition Estimation
Mohit Prashant, Arvind Easwaran, Suman Das +1
An issue concerning the use of deep reinforcement learning (RL) agents is whether they can be trusted to perform reliably when deployed, as training environments may not reflect re…
Compressing VAE-Based Out-of-Distribution Detectors for Embedded Deployment
Aditya Bansal, Michael Yuhas, Arvind Easwaran
Out-of-distribution (OOD) detectors can act as safety monitors in embedded cyber-physical systems by identifying samples outside a machine learning model's training distribution to…