Species distribution models (SDM), which quantify the correlation between the distribution of a species and environmental factors, are increasingly used to map and monitor animal and plant distributions in the context of awareness of environmental change and its ecological consequence. For perfect data, this is a straightforward classification problem from...
Easy-first, a search-based structured prediction approach, has been applied to many NLP tasks including dependency parsing and coreference resolution. This approach employs a learned greedy policy (action scoring function) to make easy decisions first, which constrains the remaining decisions and makes them easier. This thesis studies the problem of learning...
In this work, we study the problem of learning and improving policies for probabilistic planning problems. In the first part, we train neural network policies for probabilistic planning problems modeled as factored Markov decision problems. The objective is to train problem-specific neural networks via supervised learning to imitate the action...
Forty-two fall-calving crossbred cows were equally
allotted to six pens of seven cow-calf pairs each. Two
treatments (0 and 200 mg lasalocid/head/day) with three
replications were used to determine the effects of
lasalocid on fall-calving beef cows. The cows were fed
grass hay, haylage and pasture ad libitum. The cow-calf...
During exploration of imaging photosynthetic fluorimetry of Arabidopsis thaliana mutants, we discovered a novel phenomenon wherein photosynthetic efficiency (defined in Ning et al., 1995) is shown to plot in discrete groups. This exploration resulted first in the development of a spectrofluorometric method that apparently allows for in vivo observation of...
Reinforcement learning (RL) is the study of systems that learn from interaction with their environment. The current framework of Reinforcement Learning is based on receiving scalar rewards, which the agent aims to maximize. But in many real world situations, tradeoffs must be made among multiple objectives. This necessitates the use...
In the first part of this dissertation, low frequency l/f or flicker noise in the frequency range of Hz to kHz has been identified and demonstrated to be described by temperature fluctuations in heat conduction in bipolar transistors operated at higher power densities. This noise phenomenon is not described by...
In this thesis, a new learning algorithm is introduced that is targeted towards individual fairness. In order to be individually fair, mispredictions need to be avoided as each such prediction means the learning algorithm was unfair towards some individual. Therefore, achieving individual fairness implies having a perfect classifier, which is...
Ecological domains seeking to understand the environment and the behavior of species have received little attention in machine learning (ML), despite the fact that environmental changes have a significant impact on humans as well as ecosystems. Some ecological problems can be formulated similarly to other common ML applications, but there...
Western juniper (Juniperus occidentalis) encroachment has been associated with negative ecological and hydrological consequences including reductions in herbaceous production and diversity, deterioration for wildlife habitat, and higher erosion and runoff potentials. As a result, western juniper removal is a common and accepted rangeland management practice. Although studies evaluating the ecological...