1 citations · 1 across the 8 of their papers we have counts for
8 papers
When Does Text Inform? Benchmarking Information-Theoretic Metrics for Multimodal Time-Series Forecasting
Emma Andrews, Gianmarco Mengaldo
Multimodal forecasting models that combine time series with text annotations promise richer prediction through textual context, but how do we know whether a text annotation meaning…
Quantum Interval Bound Propagation for Certified Training of Quantum Neural Networks
Emma Andrews, Nahyeon Kim, Prabhat Mishra
Quantum machine learning is a promising field for efficiently learning features of a dataset to perform a specified task, such as classification. Interval bound propagation (IBP) i…
Controlled Steering-Based State Preparation for Adversarial-Robust Quantum Machine Learning
Sahan Sanjaya, Hari Krishna Parvatham, Emma Andrews +1
Quantum machine learning (QML) provides a promising framework for leveraging quantum-mechanical effects in learning tasks. However, its vulnerability to adversarial perturbations r…
Efficient Mutation Testing of Quantum Machine Learning Models
Emma Andrews, Prabhat Mishra
Quantum machine learning integrates the strengths of quantum computing and machine learning, enabling models to learn complex features using fewer parameters than their classical c…
Defending Quantum Classifiers against Adversarial Perturbations through Quantum Autoencoders
Emma Andrews, Sahan Sanjaya, Prabhat Mishra
Machine learning models can learn from data samples to carry out various tasks efficiently. When data samples are adversarially manipulated, such as by insertion of carefully craft…
Quantum Masked Autoencoders for Vision Learning
Emma Andrews, Prabhat Mishra
Classical autoencoders are widely used to learn features of input data. To improve the feature learning, classical masked autoencoders extend classical autoencoders to learn the fe…
Explainable Metric Learning for Deflating Data Bias
Emma Andrews, Prabhat Mishra
Image classification is an essential part of computer vision which assigns a given input image to a specific category based on the similarity evaluation within given criteria. Whil…
Privacy-Preserving Debiasing using Data Augmentation and Machine Unlearning
Zhixin Pan, Emma Andrews, Laura Chang +1
Data augmentation is widely used to mitigate data bias in the training dataset. However, data augmentation exposes machine learning models to privacy attacks, such as membership in…