78 citations · 297 across the 42 of their papers we have counts for
44 papers
Exploiting Per-Core Leakage: Electromagnetic Side-Channel Monitoring of Multicore Architectures
Daehyeon Bae, Sujin Park, Insup Lee +4
Multicore processors are increasingly adopted in embedded systems to meet growing performance demands. However, physical side-channel analysis of multicore architectures remains un…
Distributionally Robust Statistical Verification with Imprecise Neural Networks
Souradeep Dutta, Michele Caprio, Vivian Lin +5
A particularly challenging problem in AI safety is providing guarantees on the behavior of high-dimensional autonomous systems. Verification approaches centered around reachability…
Causal Repair of Learning-enabled Cyber-physical Systems
Pengyuan Lu, Ivan Ruchkin, Matthew Cleaveland +2
Models of actual causality leverage domain knowledge to generate convincing diagnoses of events that caused an outcome. It is promising to apply these models to diagnose and repair…
Fulfilling Formal Specifications ASAP by Model-free Reinforcement Learning
Mengyu Liu, Pengyuan Lu, Xin Chen +3
We propose a model-free reinforcement learning solution, namely the ASAP-Phi framework, to encourage an agent to fulfill a formal specification ASAP. The framework leverages a piec…
Conformal Prediction Regions for Time Series using Linear Complementarity Programming
Matthew Cleaveland, Insup Lee, George J. Pappas +1
Conformal prediction is a statistical tool for producing prediction regions of machine learning models that are valid with high probability. However, applying conformal prediction…
Using Semantic Information for Defining and Detecting OOD Inputs
Ramneet Kaur, Xiayan Ji, Souradeep Dutta +5
As machine learning models continue to achieve impressive performance across different tasks, the importance of effective anomaly detection for such models has increased as well. I…
DC4L: Distribution Shift Recovery via Data-Driven Control for Deep Learning Models
Vivian Lin, Kuk Jin Jang, Souradeep Dutta +3
Deep neural networks have repeatedly been shown to be non-robust to the uncertainties of the real world, even to naturally occurring ones. A vast majority of current approaches hav…
Credal Bayesian Deep Learning
Michele Caprio, Souradeep Dutta, Kuk Jin Jang +4
Uncertainty quantification and robustness to distribution shifts are important goals in machine learning and artificial intelligence. Although Bayesian Neural Networks (BNNs) allow…
Conservative Safety Monitors of Stochastic Dynamical Systems
Matthew Cleaveland, Oleg Sokolsky, Insup Lee +1
Generating accurate runtime safety estimates for autonomous systems is vital to ensuring their continued proliferation. However, exhaustive reasoning about future behaviors is gene…
In and Out-of-Domain Text Adversarial Robustness via Label Smoothing
Yahan Yang, Soham Dan, Dan Roth +1
Recently it has been shown that state-of-the-art NLP models are vulnerable to adversarial attacks, where the predictions of a model can be drastically altered by slight modificatio…
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…
Let's Talk Through Physics! Covert Cyber-Physical Data Exfiltration on Air-Gapped Edge Devices
Matthew Chan, Nathaniel Snyder, Marcus Lucas +7
Although organizations are continuously making concerted efforts to harden their systems against network attacks by air-gapping critical systems, attackers continuously adapt and u…
PAC Prediction Sets for Meta-Learning
Sangdon Park, Edgar Dobriban, Insup Lee +1
Uncertainty quantification is a key component of machine learning models targeted at safety-critical systems such as in healthcare or autonomous vehicles. We study this problem in…
PAC-Wrap: Semi-Supervised PAC Anomaly Detection
Shuo Li, Xiayan Ji, Edgar Dobriban +2
Anomaly detection is essential for preventing hazardous outcomes for safety-critical applications like autonomous driving. Given their safety-criticality, these applications benefi…
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…
Memory Classifiers: Two-stage Classification for Robustness in Machine Learning
Souradeep Dutta, Yahan Yang, Elena Bernardis +2
The performance of machine learning models can significantly degrade under distribution shifts of the data. We propose a new method for classification which can improve robustness…
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…
Towards PAC Multi-Object Detection and Tracking
Shuo Li, Sangdon Park, Xiayan Ji +2
Accurately detecting and tracking multi-objects is important for safety-critical applications such as autonomous navigation. However, it remains challenging to provide guarantees o…
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…
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection
Ramneet Kaur, Susmit Jha, Anirban Roy +4
Machine learning methods such as deep neural networks (DNNs), despite their success across different domains, are known to often generate incorrect predictions with high confidence…
Confidence Composition for Monitors of Verification Assumptions
Ivan Ruchkin, Matthew Cleaveland, Radoslav Ivanov +4
Closed-loop verification of cyber-physical systems with neural network controllers offers strong safety guarantees under certain assumptions. It is, however, difficult to determine…
Monotonic Safety for Scalable and Data-Efficient Probabilistic Safety Analysis
Matthew Cleaveland, Ivan Ruchkin, Oleg Sokolsky +1
Autonomous systems with machine learning-based perception can exhibit unpredictable behaviors that are difficult to quantify, let alone verify. Such behaviors are convenient to cap…
Detecting OODs as datapoints with High Uncertainty
Ramneet Kaur, Susmit Jha, Anirban Roy +3
Deep neural networks (DNNs) are known to produce incorrect predictions with very high confidence on out-of-distribution inputs (OODs). This limitation is one of the key challenges…
PAC Prediction Sets Under Covariate Shift
Sangdon Park, Edgar Dobriban, Insup Lee +1
An important challenge facing modern machine learning is how to rigorously quantify the uncertainty of model predictions. Conveying uncertainty is especially important when there a…