Browsing Division of Electrical, Electronics, and Computer Science (EECS) by Subject "Machine Learning"
Now showing items 1-20 of 33
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Approximate Dynamic Programming and Reinforcement Learning - Algorithms, Analysis and an Application
(2018-08-13)Problems involving optimal sequential making in uncertain dynamic systems arise in domains such as engineering, science and economics. Such problems can often be cast in the framework of Markov Decision Process (MDP). ... -
Boolean Functional Synthesis using Gated Continuous Logic Networks
Boolean Functional Synthesis (BFS) is a well-known challenging problem in the domain of automated program synthesis from logical specifications. This problem aims to synthesize a Boolean function that is correct-by-construction ... -
Cluster Identification : Topic Models, Matrix Factorization And Concept Association Networks
(2013-09-17)The problem of identifying clusters arising in the context of topic models and related approaches is important in the area of machine learning. The problem concerning traversals on Concept Association Networks is of great ... -
Computational Protein Structure Analysis : Kernel And Spectral Methods
(2010-08-24)The focus of this thesis is to develop computational techniques for analysis of protein structures. We model protein structures as points in 3-dimensional space which in turn are modeled as weighted graphs. The problem of ... -
Deep Learning with Minimal Supervision
Abstract In recent years, deep neural networks have achieved extraordinary performance on supervised learning tasks. Convolutional neural networks (CNN) have vastly improved the state of the art for most computer vision ... -
Design and Analysis of Consistent Algorithms for Multiclass Learning Problems
(2018-08-14)We consider the broad framework of supervised learning, where one gets examples of objects together with some labels (such as tissue samples labeled as cancerous or non-cancerous, or images of handwritten digits labeled ... -
Efficient Kernel Methods For Large Scale Classification
(2011-02-22)Classification algorithms have been widely used in many application domains. Most of these domains deal with massive collection of data and hence demand classification algorithms that scale well with the size of the data ... -
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 ... -
Feature Selection under Multicollinearity & Causal Inference on Time Series
(2018-08-20)In this work, we study and extend algorithms for Sparse Regression and Causal Inference problems. Both the problems are fundamental in the area of Data Science. The goal of regression problem is to nd out the \best" ... -
High-Throughput Computational Techniques for Discovery of Application-Specific Two-Dimensional Materials
Two-dimensional (2D) materials have revolutionized the field of materials science since the successful exfoliation of graphene in 2004. Consequently, the advances in computational science have resulted in massive generic ... -
Inverse Problems in 3D Full-wave Electromagnetics
An inverse problem in Electromagnetics (EM) refers to the process of reconstructing the physical system by processing the measured data of its electromagnetic properties. Inverse problems are typically ill-posed, and this ... -
Investigating Neural Mechanisms of Word Learning and Speech Perception
Language learning and speech perception are remarkable feats performed by the human brain, involving complex neural mechanisms that allow us to understand and communicate with one another. Unravelling the mysteries of these ... -
Kernel Methods Fast Algorithms and real life applications
(Indian Institute of Science, 2005-02-08)Support Vector Machines (SVM) have recently gained prominence in the field of machine learning and pattern classification (Vapnik, 1995, Herbrich, 2002, Scholkopf and Smola, 2002). Classification is achieved by finding a ... -
Learning Algorithms Using Chance-Constrained Programs
(2010-07-08)This thesis explores Chance-Constrained Programming (CCP) in the context of learning. It is shown that chance-constraint approaches lead to improved algorithms for three important learning problems — classification with ... -
Learning Robust Support Vector Machine Classifiers With Uncertain Observations
(2015-08-19)The central theme of the thesis is to study linear and non linear SVM formulations in the presence of uncertain observations. The main contribution of this thesis is to derive robust classfiers from partial knowledge of ... -
Low Power Machine Learning Systems for Energy Efficient Edge Devices
Energy-efficient devices are essential in the world of edge computing and the tiny Machine Learning (tinyML) paradigm. Edge devices are often constrained by the available compu- tational power and hardware resource. To ... -
Machine Learning Algorithms Using Classical And Quantum Photonics
ABSTRACT In the modern day , we are witnessing two complementary trends, exponential growth in data and shrinking of chip size. The Data is approaching to 44 zettabytes by 2020 and the chips are now available with 10nm ... -
Model Extraction and Active Learning
Machine learning models are increasingly being offered as a service by big companies such as Google, Microsoft and Amazon. They use Machine Learning as a Service (MLaaS) to expose these machine learning models to the ... -
Multi-label Classification with Multiple Label Correlation Orders And Structures
(2018-06-18)Multilabel classification has attracted much interest in recent times due to the wide applicability of the problem and the challenges involved in learning a classifier for multilabeled data. A crucial aspect of multilabel ... -
Novel First-order Algorithms for Non-smooth Optimization Problems in Machine Learning
This thesis is devoted to designing efficient optimization algorithms for machine learning (ML) problems where the underlying objective function to be optimized is convex but not necessarily differentiable. Such non-smooth ...