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Found 18 papers, 7 papers with code
MedBN: Robust Test-Time Adaptation against Malicious Test Samples
In response to the emerging threat, we propose median batch normalization (MedBN), leveraging the robustness of the median for statistics estimation within the batch normalization layer during test-time inference.
PAC Prediction Sets Under Label Shift
We propose a novel algorithm for constructing prediction sets with PAC guarantees in the label shift setting.
PAC Neural Prediction Set Learning to Quantify the Uncertainty of Generative Language Models
Uncertainty learning and quantification of models are crucial tasks to enhance the trustworthiness of the models.
TRAQ: Trustworthy Retrieval Augmented Question Answering via Conformal Prediction
To address this challenge, we propose the Trustworthy Retrieval Augmented Question Answering, or $\textit{TRAQ}$, which provides the first end-to-end statistical correctness guarantee for RAG.
Angelic Patches for Improving Third-Party Object Detector Performance
Experiments demonstrate the efficacy of the partial-covering patch in solving the complex bounding box problem.
ACon$^2$: Adaptive Conformal Consensus for Provable Blockchain Oracles
To address the oracle problem, we propose an adaptive conformal consensus (ACon$^2$) algorithm that derives a consensus set of data from multiple oracle contracts via the recent advance in online uncertainty quantification learning.
Unsafe's Betrayal: Abusing Unsafe Rust in Binary Reverse Engineering via Machine Learning
Memory-safety bugs introduce critical software-security issues.
CODiT: Conformal Out-of-Distribution Detection in Time-Series Data
Machine learning models are prone to making incorrect predictions on inputs that are far from the training distribution.
PAC Prediction Sets for Meta-Learning
Uncertainty quantification is a key component of machine learning models targeted at safety-critical systems such as in healthcare or autonomous vehicles.
Towards PAC Multi-Object Detection and Tracking
Accurately detecting and tracking multi-objects is important for safety-critical applications such as autonomous navigation.
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection
We propose the new method iDECODe, leveraging in-distribution equivariance for conformal OOD detection.
Sequential Covariate Shift Detection Using Classifier Two-Sample Tests
This problem can naturally be solved using a two-sample test--- i. e., test whether the current test distribution of covariates equals the training distribution of covariates.
Detecting OODs as datapoints with High Uncertainty
We demonstrate the difference in the detection ability of these techniques and propose an ensemble approach for detection of OODs as datapoints with high uncertainty (epistemic or aleatoric).
PAC Prediction Sets Under Covariate Shift
Our approach focuses on the setting where there is a covariate shift from the source distribution (where we have labeled training examples) to the target distribution (for which we want to quantify uncertainty).
PAC Confidence Predictions for Deep Neural Network Classifiers
In our experiments, we demonstrate that our approach can be used to provide guarantees for state-of-the-art DNNs.
Calibrated Prediction with Covariate Shift via Unsupervised Domain Adaptation
Our algorithm uses importance weighting to correct for the shift from the training to the real-world distribution.
PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction
We propose an algorithm combining calibrated prediction and generalization bounds from learning theory to construct confidence sets for deep neural networks with PAC guarantees---i. e., the confidence set for a given input contains the true label with high probability.
Resilient Linear Classification: An Approach to Deal with Attacks on Training Data
Specifically, a generic metric is proposed that is tailored to measure resilience of classification algorithms with respect to worst-case tampering of the training data.
