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Fall 2013

Cheng, Hao

Due to its wide application in various fields, clustering, as a fundamental unsupervised learning problem, has been intensively investigated over the past few decades. Unfortunately, standard clustering formulations are known to be computationally intractable. Although many convex relaxations of...

Spring 2017

Aslan,Ozlem

Most machine learning problems can be posed as solving a mathematical program that describes the structure of the prediction problem, usually expressed in terms of carefully chosen losses and regularizers. However, many machine learning problems yield mathematical programs that are not convex in...

Fall 2016

Balazs, Gabor

This thesis explores theoretical, computational, and practical aspects of convex (shape-constrained) regression, providing new excess risk upper bounds, a comparison of convex regression techniques with theoretical guarantee, a novel heuristic training algorithm for max-affine representations,...

Fall 2023

Chandak, Kushagra

A matroid bandit is the online version of combinatorial optimization on a matroid, in which the learner chooses $K$ actions from a set of $L$ actions that can form a matroid basis. Many real-world applications such as recommendation systems can be modeled as matroid bandits. In such learning...

Spring 2014

Yu, Yaoliang

Many machine learning problems can be formulated under the composite minimization framework which usually involves a smooth loss function and a nonsmooth regularizer. A lot of algorithms have thus been proposed and the main focus has been on first order gradient methods, due to their...

Fall 2017

Huang, Ruitong

On the one hand, theoretical analyses of machine learning algorithms are typically performed based on various probabilistic assumptions about the data. While these probabilistic assumptions are important in the analyses, it is debatable whether such assumptions actually hold in practice. Another...

Spring 2013

Afkanpour, Arash

Multiple kernel learning (MKL) addresses the problem of learning the kernel function from data. Since a kernel function is associated with an underlying feature space, MKL can be considered as a systematic approach to feature selection. Many of the existing MKL algorithms perform kernel learning...

Spring 2013

Farhangfar, Alireza

Many machine learning algorithms learn from the data by capturing certain interesting characteristics. Decision trees are used in many classification tasks. In some circumstances, we only want to consider fixed-depth trees. Unfortunately, finding the optimal depth-d decision tree can require time...

Spring 2013

Zolghadr, Navid

This work introduces the “online probing” problem: In each round, the learner is able to purchase the values of a subset of features for the current instance. After the learner uses this information to produce a prediction for this instance, it then has the option of paying for seeing the full...

Fall 2011

Farahmand, Amir-massoud

This thesis studies the reinforcement learning and planning problems that are modeled by a discounted Markov Decision Process (MDP) with a large state space and finite action space. We follow the value-based approach in which a function approximator is used to estimate the optimal value function....