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07/18/2023
PAC Neural Prediction Set Learning to Quantify the Uncertainty of Generative Language Models
Uncertainty learning and quantification of models are crucial tasks to e...
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07/07/2023
TRAC: Trustworthy Retrieval Augmented Chatbot
Although conversational AIs have demonstrated fantastic performance, the...
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11/17/2022
ACon^2: Adaptive Conformal Consensus for Provable Blockchain Oracles
Blockchains with smart contracts are distributed ledger systems which ac...
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10/31/2022
Unsafe's Betrayal: Abusing Unsafe Rust in Binary Reverse Engineering via Machine Learning
Memory-safety bugs introduce critical software-security issues. Rust pro...
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07/24/2022
CODiT: Conformal Out-of-Distribution Detection in Time-Series Data
Machine learning models are prone to making incorrect predictions on inp...
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07/06/2022
PAC Prediction Sets for Meta-Learning
Uncertainty quantification is a key component of machine learning models...
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04/15/2022
Towards PAC Multi-Object Detection and Tracking
Accurately detecting and tracking multi-objects is important for safety-...
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01/07/2022
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection
Machine learning methods such as deep neural networks (DNNs), despite th...
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08/13/2021
Detecting OODs as datapoints with High Uncertainty
Deep neural networks (DNNs) are known to produce incorrect predictions w...
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06/17/2021
PAC Prediction Sets Under Covariate Shift
An important challenge facing modern machine learning is how to rigorous...
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11/02/2020
PAC Confidence Predictions for Deep Neural Network Classifiers
A key challenge for deploying deep neural networks (DNNs) in safety crit...
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03/30/2020
Joint Orthogonal Band and Power Allocation for Energy Fairness in WPT System with Nonlinear Logarithmic Energy Harvesting Model
Wireless power transmission (WPT) is expected to play an important role ...
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02/29/2020
Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation
Reliable uncertainty estimates are an important tool for helping autonom...
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02/23/2020
VisionGuard: Runtime Detection of Adversarial Inputs to Perception Systems
Deep neural network (DNN) models have proven to be vulnerable to adversa...
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12/31/2019
PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction
We propose an algorithm combining calibrated prediction and generalizati...
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08/10/2017
