396 citations · 1.6k across the 95 of their papers we have counts for
16 papers · 1 filter
Parameter-efficient Quantum Multi-task Learning
Hevish Cowlessur, Chandra Thapa, Tansu Alpcan +1
Multi-task learning (MTL) improves generalization and data efficiency by jointly learning related tasks through shared representations. In the widely used hard-parameter-sharing se…
CAMP in the Odyssey: Provably Robust Reinforcement Learning with Certified Radius Maximization
Derui Wang, Kristen Moore, Diksha Goel +8
Deep reinforcement learning (DRL) has gained widespread adoption in control and decision-making tasks due to its strong performance in dynamic environments. However, DRL agents are…
One-Shot Collaborative Data Distillation
William Holland, Chandra Thapa, Sarah Ali Siddiqui +2
Large machine-learning training datasets can be distilled into small collections of informative synthetic data samples. These synthetic sets support efficient model learning and re…
ST-DPGAN: A Privacy-preserving Framework for Spatiotemporal Data Generation
Wei Shao, Rongyi Zhu, Cai Yang +7
Spatiotemporal data is prevalent in a wide range of edge devices, such as those used in personal communication and financial transactions. Recent advancements have sparked a growin…
Provably Unlearnable Data Examples
Derui Wang, Minhui Xue, Bo Li +2
The exploitation of publicly accessible data has led to escalating concerns regarding data privacy and intellectual property (IP) breaches in the age of artificial intelligence. To…
Quantum-Inspired Machine Learning: a Survey
Larry Huynh, Jin Hong, Ajmal Mian +3
Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers for its potential to leverage principles of quantum mechanics within cla…
Vertical Federated Learning: Taxonomies, Threats, and Prospects
Qun Li, Chandra Thapa, Lawrence Ong +5
Federated learning (FL) is the most popular distributed machine learning technique. FL allows machine-learning models to be trained without acquiring raw data to a single point for…
An Additive Instance-Wise Approach to Multi-class Model Interpretation
Vy Vo, Van Nguyen, Trung Le +4
Interpretable machine learning offers insights into what factors drive a certain prediction of a black-box system. A large number of interpreting methods focus on identifying expla…
LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series Data
Yuanyuan Wei, Julian Jang-Jaccard, Wen Xu +3
Anomaly detection for indoor air quality (IAQ) data has become an important area of research as the quality of air is closely related to human health and well-being. However, tradi…
Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance
Praveen Joshi, Chandra Thapa, Seyit Camtepe +3
Federated Learning (FL), Split Learning (SL), and SplitFed Learning (SFL) are three recent developments in distributed machine learning that are gaining attention due to their abil…
Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things
Yansong Gao, Minki Kim, Chandra Thapa +5
Federated learning (FL) and split learning (SL) are state-of-the-art distributed machine learning techniques to enable machine learning training without accessing raw data on clien…
Understanding and Achieving Efficient Robustness with Adversarial Supervised Contrastive Learning
Anh Bui, Trung Le, He Zhao +3
Contrastive learning (CL) has recently emerged as an effective approach to learning representation in a range of downstream tasks. Central to this approach is the selection of posi…
Advancements of federated learning towards privacy preservation: from federated learning to split learning
Chandra Thapa, M. A. P. Chamikara, Seyit A. Camtepe
In the distributed collaborative machine learning (DCML) paradigm, federated learning (FL) recently attracted much attention due to its applications in health, finance, and the lat…
Evaluation of Federated Learning in Phishing Email Detection
Chandra Thapa, Jun Wen Tang, Alsharif Abuadbba +5
The use of Artificial Intelligence (AI) to detect phishing emails is primarily dependent on large-scale centralized datasets, which opens it up to a myriad of privacy, trust, and l…
SplitFed: When Federated Learning Meets Split Learning
Chandra Thapa, M. A. P. Chamikara, Seyit Camtepe +1
Federated learning (FL) and split learning (SL) are two popular distributed machine learning approaches. Both follow a model-to-data scenario; clients train and test machine learni…
Local Differential Privacy for Deep Learning
M. A. P. Chamikara, P. Bertok, I. Khalil +3
The internet of things (IoT) is transforming major industries including but not limited to healthcare, agriculture, finance, energy, and transportation. IoT platforms are continual…