23 citations · 42 across the 14 of their papers we have counts for
14 papers
RICL: Adding In-Context Adaptability to Pre-Trained Vision-Language-Action Models
Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman +1
Multi-task ``vision-language-action'' (VLA) models have recently demonstrated increasing promise as generalist foundation models for robotics, achieving non-trivial performance out…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environments
Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman +1
Building generalist agents that can rapidly adapt to new environments is a key challenge for deploying AI in the digital and real worlds. Is scaling current agent architectures the…
Automating Weak Label Generation for Data Programming with Clinicians in the Loop
Jean Park, Sydney Pugh, Kaustubh Sridhar +7
Large Deep Neural Networks (DNNs) are often data hungry and need high-quality labeled data in copious amounts for learning to converge. This is a challenge in the field of medicine…
Recovery from Adversarial Attacks in Cyber-physical Systems: Shallow, Deep and Exploratory Works
Pengyuan Lu, Lin Zhang, Mengyu Liu +4
Cyber-physical systems (CPS) have experienced rapid growth in recent decades. However, like any other computer-based systems, malicious attacks evolve mutually, driving CPS to unde…
Memory-Consistent Neural Networks for Imitation Learning
Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman +2
Imitation learning considerably simplifies policy synthesis compared to alternative approaches by exploiting access to expert demonstrations. For such imitation policies, errors aw…
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…
Improving Neural Network Robustness via Persistency of Excitation
Kaustubh Sridhar, Oleg Sokolsky, Insup Lee +1
Improving adversarial robustness of neural networks remains a major challenge. Fundamentally, training a neural network via gradient descent is a parameter estimation problem. In a…
Real-Time Detectors for Digital and Physical Adversarial Inputs to Perception Systems
Yiannis Kantaros, Taylor Carpenter, Kaustubh Sridhar +3
Deep neural network (DNN) models have proven to be vulnerable to adversarial digital and physical attacks. In this paper, we propose a novel attack- and dataset-agnostic and real-t…