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Exploring Welfare Maximization and Fairness in Participatory Budgeting
Participatory budgeting (PB) is a voting paradigm for distributing a divisible resource, usually called a budget, among a set of projects by aggregating the preferences of individuals over these projects. It is implemented ...
Decentralized information flow control for the robot operating system
The Robot Operating System (ROS) is a popular open-source middleware widely used in the
robotics community. While ROS provides extensive support for robotic application develop-
ment, it lacks certain fundamental security ...
An Explainable Hierarchical Class Attention Model for Legal Appeal Automation
Judicial systems worldwide are overburdened due to the limited number of legal professionals.
The digitization of legal processes has resulted in abundant legal data, paving the way for the
development of legal automation ...
Topological Structures and Operators for Bivariate Data Visualization
Understanding complex phenomena in diverse scientific and engineering disciplines often relies on decoding the interplay among real-valued or scalar fields that are measured or computed over a spatial domain. This thesis ...
Rational Secure Computation: New Definitions and Constructions
Cryptography and Game Theory are two fascinating areas of modern computing, and there have been numerous works since the early 2000s to bridge these. While cryptography provides mechanisms to detect deviations, game theory ...
Bandit Algorithms: Fairness, Welfare, and Applications in Causal Inference
We study regret in online learning from a welfarist perspective and explore an application of
bandit algorithms in causal inference. We introduce Nash regret, which measures the difference
between the optimal action ...
Exploring Fairness and Causality in Online Decision-Making
Online decision-making under uncertainty is a fundamental aspect of numerous real-world problems across various domains, including online resource allocation, crowd-sourcing, and online advertising. Multi-Armed Bandits ...