Browsing Computer Science and Automation (CSA) by Subject "Research Subject Categories::TECHNOLOGY::Information technology::Computer science::Computer science"
Now showing items 1-20 of 27
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Algorithms for Achieving Fairness and Efficiency in Matching Problems
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 ... -
Algorithms for Fair Decision Making: Provable Guarantees and Applications
The topic of fair allocation of indivisible items has received significant attention because of its applicability in several real-world settings. This has led to a vast body of work focusing on defining appropriate fairness ... -
Algorithms for Social Good in Online Platforms with Guarantees on Honest Participation and Fairness
Recent decades have seen a revolution in the way people communicate, buy products, learn new things, and share life experiences. This has spurred the growth of online platforms that enable users from all over the globe to ... -
Analysis and Methods for Knowledge Graph Embeddings
Knowledge Graphs (KGs) are multi-relational graphs where nodes represent entities, and typed edges represent relationships among entities. These graphs store real-world facts such as (Lionel Messi, plays-for-team, Barcelona) ... -
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 ... -
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 ... -
Approximation Algorithms for Geometric Packing Problems
We study approximation algorithms for the geometric bin packing problem and its variants. In the two-dimensional geometric bin packing problem (2D GBP), we are given n rectangular items and we have to compute an axis-parallel ... -
Average Reward Actor-Critic with Deterministic Policy Search
The average reward criterion is relatively less studied as most existing works in the Reinforcement Learning literature consider the discounted reward criterion. There are few recent works that present on-policy average ... -
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 ... -
Constant-rate Non-malleable Codes and their Applications
Non-malleable codes(NMC) introduced by Dziembowski, Pietrzak and Wichs in ITCS 2010, provide powerful security guarantees where error-correcting codes can not provide any guarantee: a decoding of tampered codeword is ... -
Deep Learning Models for Few-shot and Metric Learning
Deep neural network-based models have achieved unprecedented performance levels over many tasks in the traditional supervised setting and scale well with large quantities of data. On the other hand, improving performance ... -
An Evaluation of Basic Protection Mechanisms in Financial Apps on Mobile Devices
This thesis concerns the robustness of security checks in financial mobile applications (or simply financial apps). The best practices recommended by OWASP for developing such apps demand that developers include several ... -
FA RCU: Fault Aware Read-Copy-Update
Deferred freeing is the fundamental technique used in Read-Copy-Update (RCU) synchronization technique where reclamation of resources is deferred until the completion of all active RCU read-side critical sections. We observe ... -
Improved Algorithms for Variants of Bin Packing and Knapsack
We study variants of two classical optimization problems: Bin Packing and Knapsack. Both bin packing and knapsack fall under the regime of "Packing and Covering Problems". In bin packing, we are given a set of input items, ... -
Inducing Constraints in Paraphrase Generation and Consistency in Paraphrase Detection
Deep learning models typically require a large volume of data. Manual curation of datasets is time-consuming and limited by imagination. As a result, natural language generation (NLG) has been employed to automate the ... -
Learning to Adapt Policies for uSD card
Machine Learning(ML) for Systems is a new and promising research area where performance of computer systems is optimized using machine learning methods. ML for Systems has outperformed traditional heuristics methods in ... -
Mitigating Bias via Algorithms with Fairness Guarantees
The rapid integration of automated decision-making systems in critical domains such as resume screening, loan approval, content recommendation, and disaster containment has raised significant concerns regarding biases in ... -
Model Checking Temporal Properties of Presburger Counter Systems
Counter systems are a well-known and powerful modeling notation for specifying infnite state systems. In this thesis we target the problem of checking temporal properties of counter systems. We address three predominant ... -
Multi-timescale and Multi-agent Reinforcement Learning Algorithms
This thesis presents six novel works involving several research domains, such as reinforcement learning (RL)– both with or without function approximators including deep neural networks, multi-agent RL, stochastic optimization, ... -
Operating System Support for Efficient Virtual Memory
Computers rely on the virtual memory abstraction to simplify programming, portability, physical memory management and ensure isolation among co-running applications. However, it creates a layer of indirection in the ...