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 ...

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 ...

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 ...

Database software vendors often need to generate synthetic databases for a variety of applications, including (a) Testing database engines and applications, (b) Data masking, (c) Benchmarking, (d) Creating what-if scenarios, ...

Matching problems arise in numerous practical settings. Fairness and efficiency are two desirable properties in most such real world scenarios. This dissertation work presents new approaches and models for capturing and ...

Legal Judgment Prediction (LJP) implements Natural Language Processing (NLP) techniques to predict judgment results based on fact description. It can play a vital role as a legal assistant and benefit legal practitioners ...

Multi-Armed Bandits (MAB) is a popular framework for modelling sequential decision-making problems under uncertainty. This thesis is a compilation of two independent works on MABs. 1. In the first work, we study a ...

E-commerce companies are actively considering the use of delivery drones for customer fulfillment, leading to growing concerns around citizen privacy. Drones are equipped with cameras, and the video feed from these cameras ...

Software developers often make queries about the security, performance effectiveness, and maintainability of their code. Through an iterative debugging process, developers analyze the code to find answers to these ...

Secure multiparty computation (MPC) allows n distrustful parties to jointly compute a function on their inputs while keeping their inputs private. The distrust is modelled as an adversary that controls up to t parties and ...