Computer Science and Automation (CSA): Recent submissions
Now showing items 1-20 of 391
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Reasoning about Safety of Camera-Based Neural Network Controlled Systems
Autonomous technologies are becoming increasingly prevalent due to the numerous benefits they offer, including improved safety and security, enhanced accessibility for mobility-challenged individuals, and the ability to ... -
Combinatorial Problems arising in Quantum Physics and Model counting
This thesis investigates some combinatorial problems arising in quantum physics and model counting. In particular, we develop new graph-theoretic techniques to resolve some open questions from quantum photonics. We also ... -
Algorithms for various cost criteria in Reinforcement Learning
In this thesis we will look at various Reinforcement Learning algorithms. We will look at algorithms for various cost criteria or reward objectives namely Finite Horizon, Discounted Cost, Risk-Sensitive Cost. For Finite ... -
Feature Tracking and Visual Analysis of Temporal Scalar Fields in the Ocean
The Bay of Bengal (BoB) has maintained its salinity distribution over the years despite a continuous flow of fresh water entering it through rivers on the northern coast, which can dilute its salinity. This can be ... -
Loop Transformations for Multi-/Many-Core Architectures using Machine Learning
Loop transformation techniques such as loop tiling, loop interchange and unroll-and-jam help expose better coarse-grain and fine-grain data-level parallelisms as well as exploit data locality. These transformations are ... -
Fair and Efficient Dynamic Memory De-bloating
The virtual memory abstraction simplifies programming and enhances portability but requires the processor to translate virtual addresses to physical addresses which can be expensive. To speed up the virtual-to-physical ... -
PyGraph: Compiler Support for Efficient and Transparent Use of CUDA Graphs
CUDA Graphs --- a recent hardware feature introduced for NVIDIA GPUs --- aim to reduce CPU launch overhead by capturing and launching a series of GPU tasks (kernels) as a DAG. However, deploying CUDA Graphs faces several ... -
Advancing the Communication Complexity Landscape of Perfectly Secure Multiparty Computation
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 ... -
Zero-Knowledge Proofs with Enhanced Deniability
In cryptography, deniability is a crucial concept that allows a participant to plausibly deny taking part in executing a scheme or protocol. Non-interactive zero-knowledge (NIZK) proofs enable a party (the prover) to ... -
Ankora: Notions of Multi-party Computation and Zero-knowledge Beyond Conventional Models
In the era of digitalization, data privacy and integrity are of utmost importance. Secure multiparty computation (MPC) and zero-knowledge (ZK) facilitate data privacy and integrity. MPC enables privacy-preserving collaborative ... -
Single and Multi-Agent Finite Horizon Reinforcement Learning Algorithms for Smart Grids
In this thesis, we study sequential decision-making under uncertainty in the context of smart grids using reinforcement learning. The underlying mathematical model for reinforcement learning algorithms are Markov Decision ... -
Analysis, Design, and Acceleration of DNN Training Preprocessing Pipelines
The performance of deep neural network (DNN) training is a function of both the training compute latency and the latency to fetch and preprocess the input data needed to train the model. With advance- ments in GPUs and ... -
Mean Field Based Investigations for Inducing Socially Optimal Epidemic Behavior in Rational Individuals
In the recent years, the world has been devastated by multiple pandemics arising out of different variants of the Corona virus. When an epidemic or pandemic strikes, it is important to move quickly to contain and suppress ... -
Barrier Function Inspired Reward Shaping in Reinforcement Learning
Reinforcement Learning (RL) has progressed from simple control tasks to complex real-world challenges with large state spaces. During initial iterations of training in most Reinforcement Learning (RL) algorithms, agents ... -
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 ... -
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 ... -
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 ... -
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 ... -
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 ...