78 citations · 510 across the 71 of their papers we have counts for
14 papers · 1 filter
Scenario Generation for Risk-Aware Reinforcement Learning with Probably Approximately Safe Guarantees
Mohit Prashant, Arvind Easwaran
Guaranteeing safety is critical to the deployment of reinforcement learning (RL) agents in the real-world, especially as policies learned using deep RL may demonstrate susceptibili…
Approximation-Free Differentiable Oblique Decision Trees
Subrat Prasad Panda, Blaise Genest, Arvind Easwaran
Decision Trees (DTs) are widely used in safety-critical domains such as medical diagnosis, valued for their interpretability and effectiveness on tabular data. However, training ac…
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…
Improving Reinforcement Learning Sample-Efficiency using Local Approximation
Mohit Prashant, Arvind Easwaran
In this study, we derive Probably Approximately Correct (PAC) bounds on the asymptotic sample-complexity for RL within the infinite-horizon Markov Decision Process (MDP) setting th…
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…
Vanilla Gradient Descent for Oblique Decision Trees
Subrat Prasad Panda, Blaise Genest, Arvind Easwaran +1
Decision Trees (DTs) constitute one of the major highly non-linear AI models, valued, e.g., for their efficiency on tabular data. Learning accurate DTs is, however, complicated, es…
Co-Design of Out-of-Distribution Detectors for Autonomous Emergency Braking Systems
Michael Yuhas, Arvind Easwaran
Learning enabled components (LECs), while critical for decision making in autonomous vehicles (AVs), are likely to make incorrect decisions when presented with samples outside of t…
Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder
Zahra Rahiminasab, Michael Yuhas, Arvind Easwaran
Out-of-distribution (OOD) detection, i.e., finding test samples derived from a different distribution than the training set, as well as reasoning about such samples (OOD reasoning)…
Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems
Michael Yuhas, Daniel Jun Xian Ng, Arvind Easwaran
When machine learning (ML) models are supplied with data outside their training distribution, they are more likely to make inaccurate predictions; in a cyber-physical system (CPS),…
Efficient Out-of-Distribution Detection Using Latent Space of -VAE for Cyber-Physical Systems
Shreyas Ramakrishna, Zahra Rahiminasab, Gabor Karsai +2
Deep Neural Networks are actively being used in the design of autonomous Cyber-Physical Systems (CPSs). The advantage of these models is their ability to handle high-dimensional st…
Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time Applications
Yeli Feng, Daniel Jun Xian Ng, Arvind Easwaran
Uncertainties in machine learning are a significant roadblock for its application in safety-critical cyber-physical systems (CPS). One source of uncertainty arises from distributio…
WiP Abstract : Robust Out-of-distribution Motion Detection and Localization in Autonomous CPS
Yeli Feng, Arvind Easwaran
Highly complex deep learning models are increasingly integrated into modern cyber-physical systems (CPS), many of which have strict safety requirements. One problem arising from th…