A GENERAL MODEL FOR NOISY LABELS IN MACHINE LEARNING

Machine learning is an ever-growing and increasingly pervasive presence in every-day life; we entrust these models, and systems built on these models, with some of our most sensitive information and security applications. However, for all of the trust that we place in these models, it is essential to recognize the fact that such models are simply reflections of the data and labels on which they are trained. To wit, if the data and labels are suspect, then so too must be the models that we rely on—yet, as larger and more comprehensive datasets become standard in contemporary machine learning, it becomes increasingly more difficult to obtain reliable, trustworthy label information. While recent work has begun to investigate mitigating the effect of noisy labels, to date this critical field has been disjointed and disconnected, despite the common goal. In this work, we propose a new model of label noise, which we call “labeler-dependent noise (LDN).” LDN extends and generalizes the canonical instance-dependent noise model to multiple labelers, and unifies every pre-ceding modeling strategy under a single umbrella. Furthermore, studying the LDN model leads us to propose a more general, modular framework for noise-robust learning called “labeler-aware learning (LAL).” Our comprehensive suite of experiments demonstrate that unlike previous methods that are unable to remain robust under the general LDN model, LAL retains its full learning capabilities under extreme, and even adversarial, conditions of label noise. We believe that LDN and LAL should mark a paradigm shift in how we learn from labeled data, so that we may both discover new insights about machine learning, and develop more robust, trustworthy models on which to build our daily lives

Conical Representations for Direct Limits of Riemannian Symmetric Spaces.

We extend the definition of conical representations for Riemannian symmetric space to a certain class of infinite-dimensional Riemannian symmetric spaces. Using an infinite-dimensional version of Weyl\u27s Unitary Trick, there is a correspondence between smooth representations of infinite-dimensional noncompact-type Riemannian symmetric spaces and smooth representations of infinite-dimensional compact-type symmetric spaces. We classify all smooth conical representations which are unitary on the compact-type side. Finally, a new class of non-smooth unitary conical representations appears on the compact-type side which has no analogue in the finite-dimensional case. We classify these representations and show how to decompose them into direct integrals of irreducible conical representations

OpinionRank: Extracting Ground Truth Labels from Unreliable Expert Opinions with Graph-Based Spectral Ranking

As larger and more comprehensive datasets become standard in contemporary machine learning, it becomes increasingly more difficult to obtain reliable, trustworthy label information with which to train sophisticated models. To address this problem, crowdsourcing has emerged as a popular, inexpensive, and efficient data mining solution for performing distributed label collection. However, crowdsourced annotations are inherently untrustworthy, as the labels are provided by anonymous volunteers who may have varying, unreliable expertise. Worse yet, some participants on commonly used platforms such as Amazon Mechanical Turk may be adversarial, and provide intentionally incorrect label information without the end user\u27s knowledge. We discuss three conventional models of the label generation process, describing their parameterizations and the model-based approaches used to solve them. We then propose OpinionRank, a model-free, interpretable, graph-based spectral algorithm for integrating crowdsourced annotations into reliable labels for performing supervised or semi-supervised learning. Our experiments show that OpinionRank performs favorably when compared against more highly parameterized algorithms. We also show that OpinionRank is scalable to very large datasets and numbers of label sources, and requires considerably fewer computational resources than previous approaches

Developmental Level of Moral Judgment Influences Behavioral Patterns during Moral Decision-making

We developed and tested a behavioral version of the Defining Issues Test-1 revised (DIT-1r), which is a measure of the development of moral judgment. We conducted a behavioral experiment using the behavioral Defining Issues Test (bDIT) to examine the relationship between participants’ moral developmental status, moral competence, and reaction time when making moral judgments. We found that when the judgments were made based on the preferred moral schema, the reaction time for moral judgments was significantly moderated by the moral developmental status. In addition, as a participant becomes more confident with moral judgment, the participant differentiates the preferred versus other schemas better particularly when the participant’s abilities for moral judgment are more developed

Measuring Moral Reasoning using Moral Dilemmas: Evaluating Reliability, Validity, and Differential Item Functioning of the Behavioral Defining Issues Test (bDIT)

We evaluated the reliability, validity, and differential item functioning (DIF) of a shorter version of the Defining Issues Test-1 (DIT-1), the behavioral DIT (bDIT), measuring the development of moral reasoning. 353 college students (81 males, 271 females, 1 not reported; age M = 18.64 years, SD = 1.20 years) who were taking introductory psychology classes at a public University in a suburb area in the Southern United States participated in the present study. First, we examined the reliability of the bDIT using Cronbach’s α and its concurrent validity with the original DIT-1 using disattenuated correlation. Second, we compared the test duration between the two measures. Third, we tested the DIF of each question between males and females. Findings reported that first, the bDIT showed acceptable reliability and good concurrent validity. Second, the test duration could be significantly shortened by employing the bDIT. Third, DIF results indicated that the bDIT items did not favour any gender. Practical implications of the present study based on the reported findings are discussed

Brahms\u27 Two Piano Concertos

Braden Auditorium Sunday Afternoon September 21, 1997 3:00p.m

Illinois State University Symphony Orchestra

Center for the Performing Arts Sunday Afternoon September 22, 2002 3:00p.m

Organic semiconductor laser biosensor : design and performance discussion

Organic distributed feedback lasers can detect nanoscale materials and are therefore an attractive sens- ing platform for biological and medical applications. In this paper, we present a model for optimizing such laser sensors and discuss the advantages of using an organic semiconductor as the laser material in comparison to dyes in a matrix. The structure of the sensor and its operation principle are described. Bulk and surface sensing exper- imental data using oligofluorene truxene macromolecules and a conjugated polymer for the gain region is shown to correspond to modeled values and is used to assess the biosensing attributes of the sensor. A comparison between organic semiconductor and dye-doped laser sensitivity is made and analyzed theoretically. Finally, experimental and theoretical specific biosensing data is provided and methods for improving sensitivity are discussed