Population trends and patterns in species distributions are the major currencies used to examine responses by biodiversity to changing environments. Effective conservation recommendations require that models of both distribution dynamics and population trends accurately reflect reality. However, identification of the appropriate temporal and spatial scales of animal response, and then...
This thesis includes three studies involving different aspects of modeling protein structure. The first study illustrates the levels of insight available from atomic-resolution protein structures. The second study derives general trends of protein geometry from atomic-resolution structures and shows their implications for modeling. The third study creates a model of...
Object categorization is one of the fundamental topics in computer vision research. Most current work in object categorization aims to discriminate among generic object classes with gross differences. However, many applications require much finer distinctions. This thesis focuses on the design, evaluation and analysis of learning algorithms for fine- grained...
Markov models are commonly used for joint inference of label sequences. Unfortunately, inference scales quadratically in the number of labels, which is problematic for training methods where inference is repeatedly preformed and is the primary computational bottleneck for large label sets. Recent work has used output coding to address this...
In open set recognition, a classifier must label instances of known classes while detecting instances of unknown classes not encountered during training. To detect unknown classes while still generalizing to new instances of existing classes, this thesis introduces a dataset augmentation technique called counterfactual image generation. This approach, based on...
Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature induction.
In the direction of efficient training, we study the training of conditional random fields (CRFs), which provide a flexible and powerful model for sequential...
This thesis studies the problem of structured prediction (SP), where the agent needs to predict a structured output for a given structured input (e.g., Part-of-Speech tagging sequence for an input sentence). Many important applications including machine translation in natural language processing (NLP) and image interpretation in computer vision can be...
Bayesian Optimization (BO) methods are often used to optimize an unknown function f(•) that is costly to evaluate. They typically work in an iterative manner. In each iteration, given a set of observation points, BO algorithms select k ≥ 1 points to be evaluated. The results of those points are...
Many methods have been explored in the literature of multi-label learning, ranging from simple problem transformation to more complex method that capture correlation among labels. However, mostly all existing works do not address the challenge with incomplete label data. The goal of this project is to extend the work of...
Question Answering in natural language processing has achieved significant progress in recent years. Yet, training and testing set methodology to evaluate the language models has proved inadequate. Adversarial examples aid us in finding loopholes inside these models and provide insights into their inner workings. In this work, an evaluation based...