Internet Advertising and the Generalized Second Price Auction: Selling Billions of Dollars Worth of Keywords

We investigate the "generalized second price" auction (GSP), a new mechanism which is used by search engines to sell online advertising that most Internet users encounter daily. GSP is tailored to its unique environment, and neither the mechanism nor the environment have previously been studied in the mechanism design literature. Although GSP looks similar to the Vickrey-Clarke-Groves (VCG) mechanism, its properties are very different. In particular, unlike the VCG mechanism, GSP generally does not have an equilibrium in dominant strategies, and truth-telling is not an equilibrium of GSP. To analyze the properties of GSP in a dynamic environment, we describe the generalized English auction that corresponds to the GSP and show that it has a unique equilibrium. This is an ex post equilibrium that results in the same payoffs to all players as the dominant strategy equilibrium of VCG.

Increasing Lapses in Data Security: The Need for a Common Answer to What Constitutes Standing in a Data Breach Context

As the number of data breaches continues to rise in the United States, so does the amount of data breach litigation. Many potential plaintiffs who suffered as victims of data breaches, however, find themselves in limbo regarding the issue of standing before a court because of a significant split on standing determinations amongst the federal circuit courts. Thus, while victims of data breaches oftentimes have their personal information fall into the hands of nefarious characters who intend to use the information to a victim’s detriment, that may not be enough to provide victims a right to sue in federal court because of disparate interpretations of standing that create impediments to data breach litigation. This Note examines conflicting holdings of various circuits on issues of standing in data breach contexts and proposes a uniform solution. It posits that applying the “heightened risk of harm” standard to standing would allow victims of stolen personal information to seek recourse in a reasonable set of situations without placing an unfair burden on the breached entities to defend against an avalanche of lawsuits. A “heightened risk of harm” standard would consistently create uniformity in the courts by placing entities that are responsible for the personally identifiable information of others (as defined by each state’s data breach notification statute) on notice that they need adequate security measures to guard against breaches and that they must prepare for lawsuits should those measures be lacking, even if personally identifying information has yet to be used

Cooperating Music Teachers’ Opinions Regarding the Importance of Selected Traits as Predictors of Successful Student Teaching Experiences

Title from PDF of title page, viewed on June 20, 2016Dissertation advisor: Charles RobinsonVitaIncludes bibliographical references (pages 214-227)Thesis (Ph.D.)--Conservatory of Music and Dance and School of Education. University of Missouri--Kansas City, 2016The purpose of the study was to determine the perceptions of cooperating mentor teachers regarding the importance of certain teacher traits as predictors of a successful student teaching experience. The data collection tool used in this study was an online survey which participants could complete online in approximately 10-15 minutes. The entire survey included 91 total questions; however, participants were presented with 54 questions to answer based on their responses to previous questions. The 54 questions included a consent statement, 40 four-point Likert-type scale responses, three multiple-selection questions, three open-ended responses, and seven demographic questions. The population targeted for this study was cooperating mentor teachers for preservice music education majors throughout the United States. Recruitment methods for this study included a combination of snowball sampling and an email soliciting participation that was sent nation-wide to music educators across the United States through the National Association for Music Education (NAfME). The snowball sampling method resulted in approximately 100 participants and the rest were recruited through the solicitation sent email by NAfME. Surveys from participants who either did not complete the survey fully, or who did not fit the inclusion criteria were discarded, resulting in a total of 519 surveys analyzed for this study. A combination of descriptive and inferential statistics was used to analyze participant data. Descriptive data were utilized to construct ranked lists of teacher traits based on the mean importance ratings of each respondent group. Inferential statistics used in this study included Analysis of Variance (ANOVA) tests and post-hoc protected t-tests. Cooperating teachers assigned highest importance ratings to the following teacher traits: demonstrating appropriate social behavior, stress management, fostering appropriate student behavior, establishing a positive rapport with others, and enthusiasm. Comparisons among band, orchestra, choral and general music teachers yielded the most variability when examining teacher traits as ordered lists based on the mean ratings of cooperating teachers. All participant groups rated personal traits as most important, followed by teaching traits, then musical traits. Content analyses of open-ended questions revealed that no teacher traits had a universal meaning or description among participants in this study.The research problem -- Literature review -- Methodology -- Results -- Discussion and implications -- Appendix A. Descriptive Statistics for All Traits by Demographic Factor -- Appendix B. Content Analyses of Teacher Trait Descriptor Responses-- Appendix C. IRB Letter of Exempt Determination -- Appendix D. NAfME Participant Recruitment Letter -- Appendix E. Survey Instrumen

Feature emergence via margin maximization: case studies in algebraic tasks

Understanding the internal representations learned by neural networks is a cornerstone challenge in the science of machine learning. While there have been significant recent strides in some cases towards understanding how neural networks implement specific target functions, this paper explores a complementary question -- why do networks arrive at particular computational strategies? Our inquiry focuses on the algebraic learning tasks of modular addition, sparse parities, and finite group operations. Our primary theoretical findings analytically characterize the features learned by stylized neural networks for these algebraic tasks. Notably, our main technique demonstrates how the principle of margin maximization alone can be used to fully specify the features learned by the network. Specifically, we prove that the trained networks utilize Fourier features to perform modular addition and employ features corresponding to irreducible group-theoretic representations to perform compositions in general groups, aligning closely with the empirical observations of Nanda et al. and Chughtai et al. More generally, we hope our techniques can help to foster a deeper understanding of why neural networks adopt specific computational strategies

Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

Watermarking generative models consists of planting a statistical signal (watermark) in a model's output so that it can be later verified that the output was generated by the given model. A strong watermarking scheme satisfies the property that a computationally bounded attacker cannot erase the watermark without causing significant quality degradation. In this paper, we study the (im)possibility of strong watermarking schemes. We prove that, under well-specified and natural assumptions, strong watermarking is impossible to achieve. This holds even in the private detection algorithm setting, where the watermark insertion and detection algorithms share a secret key, unknown to the attacker. To prove this result, we introduce a generic efficient watermark attack; the attacker is not required to know the private key of the scheme or even which scheme is used. Our attack is based on two assumptions: (1) The attacker has access to a "quality oracle" that can evaluate whether a candidate output is a high-quality response to a prompt, and (2) The attacker has access to a "perturbation oracle" which can modify an output with a nontrivial probability of maintaining quality, and which induces an efficiently mixing random walk on high-quality outputs. We argue that both assumptions can be satisfied in practice by an attacker with weaker computational capabilities than the watermarked model itself, to which the attacker has only black-box access. Furthermore, our assumptions will likely only be easier to satisfy over time as models grow in capabilities and modalities. We demonstrate the feasibility of our attack by instantiating it to attack three existing watermarking schemes for large language models: Kirchenbauer et al. (2023), Kuditipudi et al. (2023), and Zhao et al. (2023). The same attack successfully removes the watermarks planted by all three schemes, with only minor quality degradation.Comment: Blog post: https://www.harvard.edu/kempner-institute/2023/11/09/watermarking-in-the-sand