[ad_1] Introduction: Causal inference has become increasingly important in the field of recommendation systems, as it allows us to understand not just correlations between user preferences and recommendations, but also the causal relationships that drive…
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[ad_1] Introduction: Adversarial Machine Learning has emerged as a critical area of research in the field of cybersecurity defense. As attackers become more sophisticated in their methods, it is imperative for defenders to leverage machine…
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[ad_1] Introduction: Federated Transfer Learning for Collaborative Modeling is a cutting-edge research field that combines transfer learning and federated learning techniques to improve model performance in collaborative settings. This thesis aims to explore the potential…
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[ad_1] Introduction: Active Learning is a machine learning approach that aims to efficiently label large datasets by selecting the most informative data points for manual annotation. In this thesis, we will explore the use of…
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[ad_1] Introduction: Continual Learning is an important aspect of machine learning that allows models to adapt to evolving data over time. In a rapidly changing world where data is constantly being updated and new trends…
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[ad_1] Introduction: Differentially Private Machine Learning is a rapidly growing field in the realm of data privacy, especially when dealing with sensitive data. With the increasing concerns about data breaches and privacy violations, there is…
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[ad_1] Introduction: Bayesian Optimization is a powerful technique used in machine learning for hyperparameter tuning. Hyperparameter tuning is the process of choosing the best set of parameters for a machine learning algorithm to achieve optimal…
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[ad_1] Introduction: Multi-Agent Reinforcement Learning (MARL) is a relatively new approach that involves multiple agents learning to interact and collaborate with each other in order to achieve a common goal. When applied to collaborative robotics,…
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[ad_1] Introduction: Numerical Linear Algebra plays a crucial role in solving large-scale optimization problems in various fields such as machine learning, finance, engineering, and more. By leveraging numerical methods and algorithms, researchers can tackle complex…
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[ad_1] Introduction: Differential privacy has emerged as a promising approach to address the challenges of sharing sensitive healthcare data while preserving individual privacy. As healthcare organizations continue to collect and analyze large volumes of personal…
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