VictorJuiz/thesis huggingface dataset:
Exploratory model analysis for machine learning

Machine learning is growing in importance in many different fields. However, it is still very hard for users to tune hyper-parameters when optimizing their models, or perform a comprehensive and interpretable diagnosis for complex models like deep neural nets. Existing developer tool like TensorBoard only provides limited functionality which usually visualizes model statistics based on metrics predefined before the training starts. Almost nothing can be adjusted during the training. But the real model optimization and diagnosis procedures actually involve lots of interaction and continuous experiments. To tackle those challenges, we developed a framework which allows users to perform exploratory model analysis based on the hardware accelerated machine learning toolkit BIDMach. Our system is unique that we allow users to interactively add visualizations and adjust hyper-parameters during training. Also, we use Monte Carlo style algorithms to allow users to explore the entire model space under different user preferences rather than only reaching the local optimums./nWe demonstrate the usage of our system in several real-world applications. For problems like advertisement optimization or clustering where multiple optimization objectives exist, users can incorporate secondary criteria into the model-generation process and make trade-offs in an interactive way. For deep convolution neural net diagnosis, users can use our LDAM (Langevin Dynamic Activation Maximization) algorithm to systematically explore the images that stimulate a given neuron. We conduct experiments and user studies on several public datasets to show how users from different background can benefit from our tools.

Advisor: Canny, John
Serving the Nation: Rotary and Lions Clubs, the Mexican Middle Classes, and the Post-Revolutionary State, 1920s-1960s

This dissertation studies the relationship between the Mexican conservative middle classes and the post-revolutionary state from the 1920s to the 1960s. It approaches this topic through the prism of U.S.-founded businessmen's philanthropic associations, known as international "service clubs," such as the Lions and Rotary. Beginning in the 1920s, when the first Rotary Club was established in Mexico, men and their female relatives were drawn to them because they associated Rotary and Lions clubs with "modern," cosmopolitan values. By mid-century, tens of thousands of Mexicans were members of these international clubs. During Mexico's unprecedented period of economic growth, known as the "Mexican Miracle," service clubs became a facet of urban life, hosting weekly luncheons, dinners, business seminars, and charitable fundraisers. Over time, however, Rotary, Lions, and other clubs also became sites of conservative political activism./nScholars have mainly examined middle-class conservatism in Mexico through formal political parties, such as the Nation Action Party (PAN), or the Catholic Church, suggesting that these two institutions offered the only outlets for conservative politics under the post-revolutionary state. This interpretation, however, posits a reactionary and institutionally-narrow view of the middle classes. It also underestimates the transnational circulation of conservative ideas, and ignores the range of ways in which the middle classes participated in politics. This dissertation approaches the question middle-class politicization by examining the U.S.-founded service clubs they populated./n"Serving the Nation" addresses three overarching questions. First, how did the Mexican middle classes independent from government-sanctioned organizations engage with the hegemonic-party state throughout the 20th century? Second, how did conservative politics develop outside of formal opposition parties and the Catholic Church? Third, what influence did U.S. international civic organizations have in the politicization of the middle classes? Based primarily on the internal files and publications of Lions, Rotary, and other clubs in Mexico, this study argues that the self-described middle classes were drawn to these businessmen's organizations and, after joining, they modified club ideology to advance a conservative political agenda and their cultural interests. By providing a crucial ideological and organizational framework, service clubs became instrumental in the politicization of the conservative middle classes. Without an effective middle-class organization, such as there were for workers and peasants, service clubs afforded the middle classes an independent vehicle to interact with the post-revolutionary state. Finally, this story also has implications for understanding the role of US non-state actors in foreign contexts. "Serving the Nation" challenges the notion that US-based service clubs were successful instruments of American cultural and economic imperialism.

Advisor: Chowning, Margaret
Strategies of Interpersonal Emotion Regulation: Other-Directed Suppression, Reappraisal, and Acceptance

My dissertation will examine individual differences in interpersonal emotion regulation strategies (Chapter 1), everyday examples of interpersonal emotion regulation strategies (Chapter 2), and experimental evidence for how interpersonal emotion regulation strategies affect recipients (Chapter 3). The beginning of my dissertation was a finding: people reported that more than 90% of the episodes in which they regulated their emotions occurred in social situations (Gross, Richards, & John, 2006). Still, nearly all studies of emotion regulation have focused on intrapersonal (self-directed) processes, and interpersonal emotion regulation processes remain understudied. Interpersonal emotion regulation can take many forms (Zaki & Williams, 2013); in my research, I focus on the efforts one person makes to influence the emotions of another person (other-directed regulation), such as a father who hugs his distressed child to calm her down, a therapist who suggests ways to think more positively about a stressful event to a ruminating patient, a friend who reframes a discriminatory act to counteract its ensuing frustration, and a romantic partner who stonewalls to keep the other from expressing anxiety about their relationship. In other words, I study the specific strategies people may use to influence the emotions of others. I began with the two strategies most studied in the self-directed regulation literature, reappraisal and suppression. I also found an additional process important in the interpersonal domain, namely acceptance (accepting another’s emotions with no attempt to change their emotions). For my dissertation, I use a multi-method approach to examine how these strategies influence social and emotional life.

Advisor: John, Oliver P
Detection limits and fluctuation results in some spiked random matrix models and pooling of discrete data

In this thesis we examine the fundamental limits of detecting and recovering a weak signal hidden in a large amount of noise. We will consider two problems. The first one pertains to principal component analysis, and is about estimating and detecting the presence of a structured low-rank signal buried inside a large noise matrix. Prominent models of this problem include the so-called spiked or deformed ensembles from random matrix theory, which are distributions over matrices of the form ``signal + noise." It is known in these models that the top eigenpair of the data matrix becomes indicative of the presence of this signal, or "spike", when and only when its strength is above a certain "spectral" threshold. A natural question is then whether it is possible to identify the spike, or even tell if it's really present in the data below this threshold. In the first part of this thesis, we will completely characterize the fundamental limits of the detection and estimation of the spike. The analysis leading to this characterization crucially relies on a recently discovered connection of this statistical problem with the mean-field theory of spin glasses. This connection provides the necessary tools to obtain precise control on the behavior of the posterior distribution of the spike as well as the fluctuations of the associated likelihood ratio process./nThe second problem we consider is that of pooling, and is about recovering a discrete signal, i.e., whose entries only take can a finite number of values, given several observation that are constrained in a combinatorial way. More precisely, in a manner akin to compressed sensing, observations of this signal can be obtained in the form of histograms: pooled measurements counting the occurrence of each symbol across subsets of the variables. We ask what is the minimal number of random measurements of this sort it takes to reconstruct the signal. In the second part of this thesis, we determine sharp upper and lower bounds on the minimal number of measurements for the signal to be essentially unique, thus in principle recoverable from the observed data. We then provide an efficient algorithm to extract it, and show that this strategy is successful only when the number of measurements is much larger than this uniqueness threshold.

Advisor: Jordan, Michael I
Hacking Imaginaries: Codeworlds and Code Work Across the U.S./Mexico Borderlands

Drawing on ethnographic fieldwork conducted between 2014 and 2016, this dissertation investigates emerging forms of hacking and tech entrepreneurship by moving between key physical sites in Mexico and the San Francisco Bay Area. The anthropology of hacking has shown that European and U.S.-based advocates of F/OSS (free and open-source software) regard formal politics as counter-productive to their technical craft, which is aimed at liberating information and technology. Anthropologists have mostly focused on an undifferentiated hacker community precisely because hackers themselves claim that markers of difference are irrelevant to their social and technical organization. But what happens when practices of hacking challenge the boundaries of colorblindness and intersect with constructions of race, nation, and class?/nTo examine how the shifting politics of hacking influence models for technology-driven capitalism, I conducted participant-observation in hackathons and co-working spaces with self-identified "hacker-entrepreneurs." At one level, my dissertation makes a comparative analysis of how communities positioned on separate sides of the U.S./Mexico border use their "code work" to make modifications to established technological and entrepreneurial protocols that themselves aim to redress economic injustices. On another level, as these two tech communities coalesce by participating in events aimed at empowering a Latina/o collective, I show how Latinidad gets constructed (and contested) across hierarchies of race, nation, and class./nScholars have long been interested in social protests and movements among Latina/os and in Latin America. I find them in unlikely places-in spaces normally thought to be advancing capitalistic accumulation. My research shifts from thinking about technological capitalism in terms of abstract models and focuses instead on the logics and subjectivities people use to structure their everyday work and social lives. I look at one phenomenon that might ordinarily be broken up into different anthropological domains (technology, racialization, capitalism, global economy) and consider how they all come together by focusing on hacking/entrepreneurship as a critical site of academic inquiry.

Advisor: Briggs, Charles L
Sets as Measures: Optimization and Machine Learning

The purpose of this thesis is to address the following simple question:/nHow do we design efficient algorithms to solve optimization or machine learning problems where the decision variable (or target label) is a set of unknown cardinality?/nIn this thesis we show that, in some cases, optimization and machine learning algorithms designed to work with single vectors can be directly applied to problems involving sets. We do this by invoking a classical trick: we lift sets to elements of a vector space, namely an infinite-dimensional space of measures. While this idea has been explored extensively in theoretical analysis, we show that it also generates efficient practical algorithms.

Advisor: Jordan, Michael
Recht, Benjamin
Machine Learning for the Developing World using Mobile Communication Metadata

Machine learning algorithms have started having an unprecedented impact on human society due to their improved accuracy. The ability to collect and analyze information at a large scale has enabled the researchers to develop novel algorithms that can beat the best of the human experts in quite a few cases. The size of the data and the quality of the data has been the primary factor behind the success of the machine learning algorithms. However, when it comes to the data related to human behavior a digital divide still exists. An indirect consequence of the popularity of the social networks and the proliferation of sensors in the developed world is that the researchers in industry and academia have been able to fine tune their findings and algorithms using these huge behavioral datasets providing accurate and deep insights about human behavior in the developed world. However, the same is not true about the developing world where until recently the surveys have been the primary way of collecting information about individuals in the society./n/nSocial networks and digital sensors have not been that common in the developing world as compared to the developed world with one big exception, i.e., the mobile phones. More than 95\% of the world population today has mobile phone coverage and event in some of the most under-developed places of the earth the penetration of mobile phones is much higher as compared to other measures of human development like literacy or access to the financial infrastructure. As a result, researchers have been using the meta-data collected by the mobile phone companies in these developing countries as an alternative to the more conventional data sources. However, the raw mobile phone data may not be very well suited for the machine learning algorithms. In other words, there is a need for algorithms to convert the raw mobile communication meta-data into features suited for the machine learning algorithms. Developing novel ways to extract features from the mobile phone meta-data has been the central question of my research./n/nIn this dissertation, I am going to describe my work on extracting features from mobile communication logs using techniques like Deterministic Finite Automata (DFA). I will also show that how this approach outperforms other methods for problems like product adoption and churn prediction. I further show that by using DFA based features and spectral analysis of the multi-view nature of mobile communication networks, advanced neural network training algorithms can be developed that beat the current state of the art methods for the problems like poverty prediction and gender prediction. Last part of this dissertation describes the value of communication networks data for research questions related to social networks analysis like what are the salient differences between the behavioral patterns of men and women in the developing world as exhibited in the communication networks data.

Advisor: Blumenstock, Joshua E
Towards Practical Privacy-Preserving Data Analytics

Organizations are increasingly collecting sensitive information about individuals. Extracting value from this data requires providing analysts with flexible access, typically in the form of databases that support SQL queries. Unfortunately, allowing access to data has been a major cause of privacy breaches./nTraditional approaches for data security cannot protect privacy of individuals while providing flexible access for analytics. This presents a difficult trade-off. Overly restrictive policies result in underutilization and data siloing, while insufficient restrictions can lead to privacy breaches and data leaks./nDifferential privacy is widely recognized by experts as the most rigorous theoretical solution to this problem. Differential privacy provides a formal guarantee of privacy for individuals while allowing general statistical analysis of the data. Despite extensive academic research, differential privacy has not been widely adopted in practice. Additional work is needed to address performance and usability issues that arise when applying differential privacy to real-world environments./nIn this dissertation we develop empirical and theoretical advances towards practical differential privacy. We conduct a study using 8.1 million real-world queries to determine the requirements for practical differential privacy, and identify limitations of previous approaches in light of these requirements. We then propose a novel method for differential privacy that addresses key limitations of previous approaches./nWe present Chorus, an open-source system that automatically enforces differential privacy for statistical SQL queries. Chorus is the first system for differential privacy that is compatible with real databases, supports queries expressed in standard SQL, and integrates easily into existing data environments./nOur evaluation demonstrates that Chorus supports 93.9% of real-world statistical queries, integrates with production databases without modifications to the database, and scales to hundreds of millions of records. Chorus is currently deployed at a large technology company for internal analytics and GDPR compliance. In this capacity, Chorus processes more than 10,000 queries per day.

Advisor: Song, Dawn
A geometric perspective on some topics in statistical learning

Modern science and engineering often generate data sets with a large sample size and a comparably large dimension which puts classic asymptotic theory into question in many ways. Therefore, the main focus of this thesis is to develop a fundamental understanding/nof statistical procedures for estimation and hypothesis testing from a non-asymptotic point of view, where both the sample size and problem dimension grow hand in hand. A range of different problems are explored in this thesis, including work on the geometry of hypothesis testing, adaptivity to local structure in estimation, effective methods for shape-constrained problems, and early stopping with boosting algorithms./nOur treatment of these different problems shares the common theme of emphasizing the underlying geometric structure./nTo be more specific, in our hypothesis testing problem, the null and alternative are specified by a pair of convex cones. This cone structure makes it possible for a sharp characterization of the behavior of Generalized Likelihood Ratio Test (GLRT) and its optimality property. The problem of planar set estimation based on noisy measurements of its support function, is a non-parametric problem in nature. It is interesting to see that estimators can be constructed such that they are more efficient in the case when the underlying set has a simpler structure, even without knowing the set beforehand. Moreover, when we consider applying boosting algorithms to estimate a function in reproducing kernel Hibert space (RKHS), the optimal stopping rule and the resulting estimator turn out to be determined by the localized complexity of the space./nThese results demonstrate that, on one hand, one can benefit from respecting and making use of the underlying structure (optimal early stopping rule for different RKHS); on the other hand, some procedures (such as GLRT or local smoothing estimators) can achieve better performance when the underlying structure is simpler, without prior knowledge of the structure itself./nTo evaluate the behavior of any statistical procedure, we follow the classic minimax framework and also discuss about more refined notion of local minimaxity.

Advisor: Wainwright, Martin J
Guntuboyina, Adityanand
The Professionalization of the Clergy in Late Antiquity

This dissertation investigates the role that the prior occupational experiences of Christian clerics played in molding the institutional development of the Christian Church in the later Roman Empire, roughly between 200 and 600 CE. It first demonstrates how socio-legal, economic, and demographic constraints limited the ordination of clerics to literate populations among the artisan, mercantile, and professional classes. The argument then proceeds to demonstrate how the organizational structures and payment practices of occupational units among these classes such as professional collegia served as models for the nascent discipline of the Christian clergy. The third chapter explores how the ordination of everyday physicians and the positive estimation of secular medicine among bishops drove the formation of ecclesiastical hospitals and the propagation of medical knowledge. The final chapter traces the development of two peculiar ecclesiastical offices, namely the ordained notary and a class of ordained lawyers called defensores ecclesiae, both of which mandated the ordination of technically trained legal practitioners. As with ordained physicians, evidence suggests that by the end of antiquity both ecclesiastical notaries and defensores were receiving their technical education within ecclesiastical communities. This evolution, in which professionals could solely acquire their skills within the Church, signaled the gradual usurpation of ecclesiastical functionaries over their lay analogs.

Advisor: Elm, Susanna
Where the I Comes From

Abstract/nWhere the I Comes From/nby/nUrsula Kwong-Brown/nDoctor of Philosophy in Music/nand the/nDesignated Emphasis in New Media/nin the/nGraduate Division/nUniversity of California, Berkeley/nProfessor Myra Melford, Chair/nI first read Josh Bell’s poem “Where the I Comes From” in the aftermath of the 2016 presidential election, at a time when it seemed like America was being torn apart by class and racial conflict. The poem spoke to me because it so honestly describes the propensity for violence which is hidden inside each of us. It is easy to blame one another for our country’s current state of xenophobia and the daily destruction of our democratic institutions, but much harder to recognize the seeds of jealousy, narcissism and hatred within ourselves. We must see our own faults clearly before we can find the common humanity from which we can address everyone – even our enemies – with love and compassion.

Advisor: Melford, Myra