4 papers · 1 filter
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…
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…