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ACTIVE LEARNING WITH RICH FEEDBACK
Title
ACTIVE LEARNING WITH RICH FEEDBACK
Creator
Sharma, Manali
Date
2017, 2017-07
Description
One of the goals of artificial intelligence is to build predictive models that can learn from examples and make predictions. Predictive models...
Show moreOne of the goals of artificial intelligence is to build predictive models that can learn from examples and make predictions. Predictive models are useful in many domains and applications such as predicting fraud in credit card transactions, predicting whether a patient has heart-disease, predicting whether an email is a spam, predicting crime, recognizing images, recognizing speech, and many more. Building predictive models often requires supervision from a human expert. Since there is a human in the loop, the supervision needs to be as resource-efficient as possible to save the human’s time, cost, and effort in providing supervision. One solution to make the supervision resource-efficient is active learning, in which the active learner interacts with the human to acquire supervision, usually in the form of labels, for a few selected examples to effectively learn a function that can be used to make predictions. In this thesis, I explore more intuitive and effective use of human supervision through richer interactions between the human expert and the learner, so that the human can understand the learner’s reasoning for querying examples, and provide information beyond just the labels for examples. Traditional active learning approaches select informative examples for labeling, but the human does not get to know why those examples are useful to the learner. While interacting with the learner to annotate examples, humans can provide rich feedback, such as provide their prior knowledge and understanding of the domain, explain certain characteristics of the data, suggest important attributes of the data, give rationales for why an example belongs to a certain category, and provide explanations by pointing out features that are indicative of certain labels. The challenge, however, is that traditional supervised learning algorithms can learn from labeled examples, but they are not equipped to readily absorb the rich feedback. In this thesis, we enable the learner to explain its reasons for selecting instances and devise novel methods to incorporate rich feedback from humans into the training of predictive models. Specifically, I build and evaluate four novel active learning frameworks to enrich the interactions between the human and learner. First, I introduce an active learning framework to reveal the learner’s perception of informative instances. Specifically, we enable the learner to provide its reasons for uncertainty on examples and utilize the learner’s perception of uncertainty to select better examples for training the predictive models. Second, I introduce a framework to enrich the interaction between the human and learner for document classification task. Specifically, we ask the human to annotate documents and provide rationales for their annotation by highlighting phrases that convinced them to choose a particular label for a document. Third, I introduce a framework to enrich the interaction between the human and learner for the aviation domain, where we ask subject matter experts to examine flights and provide rationales for why certain flights have safety concerns. Fourth, I introduce a framework to enrich the interaction between the human and learner for document classification task, where we ask humans to provide explanations for classification by highlighting phrases that reinforce their belief in the document’s label and striking-out phrases that weaken their belief in the document’s label. We show that enabling richer interactions between the human and learner and incorporating rich feedback into learning lead to more effective training of predictive models and better utilization of human supervision.
Ph.D. in Computer Science, July 2017
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Gaussian Process Assisted Active Learning of Physical Laws
Title
Gaussian Process Assisted Active Learning of Physical Laws
Creator
Chen, Jiuhai
Date
2020
Description
In many areas of science and engineering, discovering the governing differential equations from the noisy experimental data is an essential...
Show moreIn many areas of science and engineering, discovering the governing differential equations from the noisy experimental data is an essential challenge. It is also a critical step in understanding the physical phenomena and prediction of the future behaviors of the systems. However, in many cases, it is expensive or time-consuming to collect experimental data. This article provides an active learning approach to estimate the unknown differential equations accurately with reduced experimental data size. We propose an adaptive design criterion combining the D-optimality and the maximin space-filling criterion. The D-optimality involves the unknown solution of the differential equations and derivatives of the solution. Gaussian process models are estimated using the available experimental data and used as surrogates of these unknown solution functions. The derivatives of the estimated Gaussian process models are derived and used to substitute the derivatives of the solution. Variable-selection-based regression methods are used to learn the differential equations from the experimental data. The proposed active learning approach is entirely data-driven and requires no tuning parameters. Through three case studies, we demonstrate the proposed approach outperforms the standard randomized design in terms of model accuracy and data economy.
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