[ad_1] Introduction In recent times, with the rapid advancements in technology and the widespread use of cloud computing, the need for secure and confidential data processing has become more important than ever. Encrypted computation is…
Read More
[ad_1] Introduction Federated analytics is a novel approach to decentralized learning that involves training machine learning models across multiple edge devices or servers while keeping the data on these devices rather than centralizing it. This…
Read More
[ad_1] Introduction In today’s digital age, the collection, analysis, and storage of personal data have become ubiquitous in various applications such as healthcare, finance, and social media. With the increasing concerns about privacy and data…
Read More
[ad_1] Introduction The increasing demand for data privacy and security in collaborative learning environments has led to the development of secure multi-party computation (MPC) techniques. These techniques allow multiple parties to jointly compute a function…
Read More
[ad_1] Introduction: Stochastic optimization is a powerful tool used in various fields such as machine learning, operations research, and engineering to find optimal solutions in the presence of uncertainty. In many real-world scenarios, the objectives…
Read More
[ad_1] Introduction Convex optimization is a powerful mathematical tool that has been widely used in various fields such as machine learning, signal processing, control systems, and operations research. It involves the optimization of convex objective…
Read More
[ad_1] Introduction: In recent years, there has been a growing interest in submodular optimization for diversity and coverage in various fields such as machine learning, data mining, and artificial intelligence. Submodular functions have the property…
Read More
[ad_1] Introduction Dictionary learning is a powerful technique used in machine learning and signal processing for basis discovery. It involves the process of learning a dictionary that can effectively represent a set of data samples…
Read More
[ad_1] Introduction Manifold learning is a powerful technique used in machine learning and data analysis for dimensionality reduction. It aims to uncover the underlying structure of high-dimensional data by representing it in a lower-dimensional space…
Read More
[ad_1] Introduction: In the field of machine learning and pattern recognition, the measurement of similarity between data points is a crucial task with implications in various applications such as image retrieval, recommendation systems, and text…
Read More