23 citations · 42 across the 14 of their papers we have counts for
6 papers · 1 filter
Predict-and-Critic: Accelerated End-to-End Predictive Control for Cloud Computing through Reinforcement Learning
Kaustubh Sridhar, Vikramank Singh, Balakrishnan Narayanaswamy +1
Cloud computing holds the promise of reduced costs through economies of scale. To realize this promise, cloud computing vendors typically solve sequential resource allocation probl…
Guaranteed Conformance of Neurosymbolic Models to Natural Constraints
Kaustubh Sridhar, Souradeep Dutta, James Weimer +1
Deep neural networks have emerged as the workhorse for a large section of robotics and control applications, especially as models for dynamical systems. Such data-driven models are…
CODiT: Conformal Out-of-Distribution Detection in Time-Series Data
Ramneet Kaur, Kaustubh Sridhar, Sangdon Park +4
Machine learning models are prone to making incorrect predictions on inputs that are far from the training distribution. This hinders their deployment in safety-critical applicatio…
Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations
Kaustubh Sridhar, Souradeep Dutta, Ramneet Kaur +3
Adversarial training (AT) and its variants have spearheaded progress in improving neural network robustness to adversarial perturbations and common corruptions in the last few year…
A Framework for Checkpointing and Recovery of Hierarchical Cyber-Physical Systems
Kaustubh Sridhar, Radoslav Ivanov, Vuk Lesi +6
This paper tackles the problem of making complex resource-constrained cyber-physical systems (CPS) resilient to sensor anomalies. In particular, we present a framework for checkpoi…
Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates
Souradeep Dutta, Kaustubh Sridhar, Osbert Bastani +4
Long horizon robot learning tasks with sparse rewards pose a significant challenge for current reinforcement learning algorithms. A key feature enabling humans to learn challenging…