b'Insup Lee'

research

∙ 09/01/2023

Curating Naturally Adversarial Datasets for Trustworthy AI in Healthcare

Deep learning models have shown promising predictive accuracy for time-s...

0 Sydney Pugh, et al. ∙

research

∙ 08/28/2023

Distributionally Robust Statistical Verification with Imprecise Neural Networks

A particularly challenging problem in AI safety is providing guarantees ...

0 Souradeep Dutta, et al. ∙

research

∙ 07/13/2023

A Novel Bayes' Theorem for Upper Probabilities

In their seminal 1990 paper, Wasserman and Kadane establish an upper bou...

0 Michele Caprio, et al. ∙

research

∙ 07/07/2023

TRAC: Trustworthy Retrieval Augmented Chatbot

Although conversational AIs have demonstrated fantastic performance, the...

0 Shuo Li, et al. ∙

research

∙ 05/24/2023

Zero-shot Task Preference Addressing Enabled by Imprecise Bayesian Continual Learning

Like generic multi-task learning, continual learning has the nature of m...

0 Pengyuan Lu, et al. ∙

research

∙ 04/27/2023

Detection of Adversarial Physical Attacks in Time-Series Image Data

Deep neural networks (DNN) have become a common sensing modality in auto...

0 Ramneet Kaur, et al. ∙

research

∙ 04/25/2023

Fulfilling Formal Specifications ASAP by Model-free Reinforcement Learning

We propose a model-free reinforcement learning solution, namely the ASAP...

0 Mengyu Liu, et al. ∙

research

∙ 04/06/2023

Causal Repair of Learning-enabled Cyber-physical Systems

Models of actual causality leverage domain knowledge to generate convinc...

0 Pengyuan Lu, et al. ∙

research

∙ 04/03/2023

Conformal Prediction Regions for Time Series using Linear Complementarity Programming

Conformal prediction is a statistical tool for producing prediction regi...

0 Matthew Cleaveland, et al. ∙

research

∙ 02/21/2023

Using Semantic Information for Defining and Detecting OOD Inputs

As machine learning models continue to achieve impressive performance ac...

0 Ramneet Kaur, et al. ∙

research

∙ 02/20/2023

Take Me Home: Reversing Distribution Shifts using Reinforcement Learning

Deep neural networks have repeatedly been shown to be non-robust to the ...

0 Vivian Lin, et al. ∙

research

∙ 02/19/2023

Imprecise Bayesian Neural Networks

Uncertainty quantification and robustness to distribution shifts are imp...

0 Michele Caprio, et al. ∙

research

∙ 01/26/2023

Conservative Safety Monitors of Stochastic Dynamical Systems

Generating accurate runtime safety estimates for autonomous systems is v...

0 Matthew Cleaveland, et al. ∙

research

∙ 12/20/2022

In and Out-of-Domain Text Adversarial Robustness via Label Smoothing

Recently it has been shown that state-of-the-art NLP models are vulnerab...

0 Yahan Yang, et al. ∙

research

∙ 12/02/2022

Guaranteed Conformance of Neurosymbolic Models to Natural Constraints

Deep neural networks have emerged as the workhorse for a large section o...

0 Kaustubh Sridhar, et al. ∙

research

∙ 10/14/2022

Let's Talk Through Physics! Covert Cyber-Physical Data Exfiltration on Air-Gapped Edge Devices

Although organizations are continuously making concerted efforts to hard...

0 Matthew Chan, et al. ∙

research

∙ 09/09/2022

Learning Enabled Fast Planning and Control in Dynamic Environments with Intermittent Information

This paper addresses a safe planning and control problem for mobile robo...

0 Matthew Cleaveland, et al. ∙

research

∙ 07/24/2022

CODiT: Conformal Out-of-Distribution Detection in Time-Series Data

Machine learning models are prone to making incorrect predictions on inp...

9 Ramneet Kaur, et al. ∙

research

∙ 07/06/2022

PAC Prediction Sets for Meta-Learning

Uncertainty quantification is a key component of machine learning models...

0 Sangdon Park, et al. ∙

research

∙ 06/13/2022

Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations

Adversarial training (AT) and its variants have spearheaded progress in ...

15 Kaustubh Sridhar, et al. ∙

research

∙ 06/10/2022

Memory Classifiers: Two-stage Classification for Robustness in Machine Learning

The performance of machine learning models can significantly degrade und...

0 Souradeep Dutta, et al. ∙

research

∙ 05/22/2022

PAC-Wrap: Semi-Supervised PAC Anomaly Detection

Anomaly detection is essential for preventing hazardous outcomes for saf...

0 Shuo Li, et al. ∙

research

∙ 05/17/2022

A Framework for Checkpointing and Recovery of Hierarchical Cyber-Physical Systems

This paper tackles the problem of making complex resource-constrained cy...

0 Kaustubh Sridhar, et al. ∙

research

∙ 04/15/2022

Towards PAC Multi-Object Detection and Tracking

Accurately detecting and tracking multi-objects is important for safety-...

0 Shuo Li, et al. ∙

research

∙ 02/25/2022

Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates

Environments with sparse rewards and long horizons pose a significant ch...

9 Souradeep Dutta, et al. ∙

research

∙ 01/07/2022

iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection

Machine learning methods such as deep neural networks (DNNs), despite th...

0 Ramneet Kaur, et al. ∙

research

∙ 11/04/2021

Monotonic Safety for Scalable and Data-Efficient Probabilistic Safety Analysis

Autonomous systems with machine learning-based perception can exhibit un...

0 Matthew Cleaveland, et al. ∙

research

∙ 11/03/2021

Confidence Composition for Monitors of Verification Assumptions

Closed-loop verification of cyber-physical systems with neural network c...

0 Ivan Ruchkin, et al. ∙

research

∙ 08/13/2021

Detecting OODs as datapoints with High Uncertainty

Deep neural networks (DNNs) are known to produce incorrect predictions w...

0 Ramneet Kaur, et al. ∙

research

∙ 06/17/2021

PAC Prediction Sets Under Covariate Shift

An important challenge facing modern machine learning is how to rigorous...

0 Sangdon Park, et al. ∙

research

∙ 06/03/2021

Robust Learning via Persistency of Excitation

Improving adversarial robustness of neural networks remains a major chal...

0 Kaustubh Sridhar, et al. ∙

research

∙ 04/30/2021

ModelGuard: Runtime Validation of Lipschitz-continuous Models

This paper presents ModelGuard, a sampling-based approach to runtime mod...

0 Taylor J. Carpenter, et al. ∙

research

∙ 03/23/2021

Are all outliers alike? On Understanding the Diversity of Outliers for Detecting OODs

Deep neural networks (DNNs) are known to produce incorrect predictions w...

0 Ramneet Kaur, et al. ∙

research

∙ 02/25/2021

Confidence Calibration with Bounded Error Using Transformations

As machine learning techniques become widely adopted in new domains, esp...

0 Sooyong Jang, et al. ∙

research

∙ 11/09/2020

Improving Classifier Confidence using Lossy Label-Invariant Transformations

Providing reliable model uncertainty estimates is imperative to enabling...

0 Sooyong Jang, et al. ∙

research

∙ 11/02/2020

PAC Confidence Predictions for Deep Neural Network Classifiers

A key challenge for deploying deep neural networks (DNNs) in safety crit...

0 Sangdon Park, et al. ∙

research

∙ 10/12/2020

Class-Weighted Evaluation Metrics for Imbalanced Data Classification

Class distribution skews in imbalanced datasets may lead to models with ...

23 Akhilesh Gupta, et al. ∙

research

∙ 04/06/2020

Optimal Virtual Cluster-based Multiprocessor Scheduling

Scheduling of constrained deadline sporadic task systems on multiprocess...

0 Arvind Easwaran, et al. ∙

research

∙ 02/29/2020

Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation

Reliable uncertainty estimates are an important tool for helping autonom...

9 Sangdon Park, et al. ∙

research

∙ 02/23/2020

VisionGuard: Runtime Detection of Adversarial Inputs to Perception Systems

Deep neural network (DNN) models have proven to be vulnerable to adversa...

6 Yiannis Kantaros, et al. ∙

research

∙ 12/31/2019

PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction

We propose an algorithm combining calibrated prediction and generalizati...

15 Sangdon Park, et al. ∙

research

∙ 10/24/2019

Case Study: Verifying the Safety of an Autonomous Racing Car with a Neural Network Controller

This paper describes a verification case study on an autonomous racing c...

0 Radoslav Ivanov, et al. ∙

research

∙ 09/11/2019

Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guarantees

Reinforcement Learning (RL) has emerged as an efficient method of choice...

8 Mohammadhosein Hasanbeig, et al. ∙

research

∙ 06/20/2018

Self-Driving Vehicle Verification Towards a Benchmark

Industrial cyber-physical systems are hybrid systems with strict safety ...

0 Nima Roohi, et al. ∙

research

∙ 06/20/2018

Injected and Delivered: Fabricating Implicit Control over Actuation Systems by Spoofing Inertial Sensors

Inertial sensors provide crucial feedback for control systems to determi...

0 Yazhou Tu, et al. ∙

research

∙ 08/10/2017

Resilient Linear Classification: An Approach to Deal with Attacks on Training Data

Data-driven techniques are used in cyber-physical systems (CPS) for cont...

0 Sangdon Park, et al. ∙

Insup Lee

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