VictorJuiz/thesis huggingface dataset:
Adaptation Based Approaches to Distribution Shift Problems

Distribution shift in machine learning refers to the general problem where a model is evaluated on test data drawn from a different distribution than the training data distribution. In real world applications, distribution shift is oftentimes not the exception, but the norm: for example, the data distribution may change over time, the model may be tested in new and unforeseen circumstances, or distribution shift may even be a consequence of the problem definition itself. In order to realize the full potential of machine learning, therefore, effective solutions must be developed to deal with distribution shift problems./nIn this thesis, we first review the many ways that distribution shift arises in machine learning settings, using several examples to ground the problem while demonstrating the ubiquity with which the problem occurs. The first few of these examples come from control and reinforcement learning, and for these examples, distribution shift is baked in to the problem formulation itself. As the shifts are easily characterized and peripheral to the main goal, handcrafted techniques can be brought to bear to handle these cases. The subsequent examples illustrate ways in which shift creeps into real world supervised learning problems, which motivates the study and development of general purpose learning paradigms that can be used to tackle these and future examples./nThe paradigms we focus on in this thesis revolve around the concept of adaptation: leveraging the information available at test time in order to change the model to better handle the test data. We ask an open-ended question: how can and should a model adapt when faced with distribution shift? Our first proposal for answering this question is the paradigm of adaptive risk minimization: when provided examples at training time of what different shifts are likely to occur, the model should learn to adapt to these training shifts, thus better preparing itself for similar instances of test shift. We formalize and instantiate methods for this paradigm through the toolkit of meta-learning, demonstrating that these methods are competitive with, and oftentimes superior to, prior approaches for handling distribution shift. Our second proposal for answering the above question lies at the other end of the spectrum: even without access to the training procedure or multiple test points, the model can still rely on inductive biases relayed by data augmentations in order to adapt. This leads to the method of marginal entropy minimization with one test point, a broadly applicable "slot-in replacement" for standard inference that proves to be effective for a wide range of commonly used models on a number of challenging distribution shift benchmarks.

Advisor: Levine, Sergey
Data-Driven Methods for Safe Task Transfer Learning with Model Predictive Control

The topic of learning in control has garnered much attention in recent years, with many researchers proposing methods for combining data-based learning methods with more traditional control design. For systems repeatedly performing a single task, iterative learning controllers provide a structured, model-based way of using collected data to iteratively improve on a particular task while guaranteeing constraint satisfaction during the learning process. However, it remains difficult to design model-based learning controllers that both perform well and act safely in a variety of changing or unknown environments./nThis dissertation considers a particular problem: how to use stored trajectory data from a system solving an initial set of tasks in order to design a controller that performs a related task in a new environment both safely (satisfying all new constraints) and effectively (maximizing a desired objective). We approach this question from a Model Predictive Control (MPC) perspective. Fundamentally, we ask how traditionally model-based terminal sets and cost functions of the MPC may be replaced with data-driven counterparts while maintaining feasibility guarantees that classical MPC theory offers.We consider various instantiations of the changing environment problem, including known or unknown task environments with time-invariant or time-varying constraints. For each scenario, we propose approaches for safe and effective control design. Using tools from MPC theory, optimization, and statistics, we demonstrate the safety (with high probability) for each proposed control scheme. The presented control approaches are validated in simulations and experiments in a variety of applications, including autonomous racing, robotic manipulation, and computer game tasks. The evaluations demonstrate the potential for safely integrating data in model-based control design.

Advisor: Borrelli, Francesco
Constraining Ill-Posed Inverse Problems in Neural Electrophysiology via Biophysically Detailed Forward Simulation

Biophysically detailed simulation is an invaluable tool for understanding experimental data when those data do not uniquely determine the underlying state of the system, a situation we refer to as an ill-posed inverse problem. Such problems arise frequently in the study of biological systems with many degrees of freedom. This dissertation presents simulation- based approaches to two ill-posed inverse problems in neural electrophysiology. First, using a large volume of simulated data, we demonstrate that a Convolutional Neural Network can be trained to determine the conductances of various ion channels in a neuron from its somatic membrane potential in response to a current injection. Next, we use a simulation to study the cellular origin of electrical signals recorded at the surface of the brain, and find that they are produced primarily in layers V and VI of the cortex, contrary to the intuition that neurons closer to the electrode should contribute more of the signal. In both cases, simulation is a natural way to incorporate biological constraints to rule out certain a priori plausible solutions. Our results show how the massive throughput, fine-grained control over model parameters, and access to underlying ground-truth details within a simulation can be utilized to overcome the ill-posedness that many biological problems exhibit when stated in physical terms.

Advisor: Bouchard, Kristofer
DeWeese, Michael
Nonlinear Optical Properties of the Chiral Weyl Semimetal RhSi

Nonlinear optical properties of materials offer a diverse and powerful experimental probe into symmetry, electronic properties, and band structure. When paired with variable-wavelength light sources, these techniques are enhanced with the capability of resolving energy-dependent features. These properties are restricted to space groups that break inversion symmetry, but this dependence on crystal structure provides an opportunity to gain information about the material, and can help distinguish various electronic effects within a crystal. This point is crucial to this work, where bulk and surface optical properties in the Weyl semimetal RhSi are completely disentangled through symmetry considerations alone, with no need to rely on conjectures or assumptions about spectral features./nWeyl semimetals are a class of materials of special interest in condensed matter physics community within the last 10 years because their electronic band structures contain features which are analogous to Weyl fermions, a class of chiral fundamental particles which have not been observed in nature. While studied extensively experimentally, certain properties of Weyl semimetals have eluded measurement and theories have gone unconfirmed. One issue is that most Weyl semimetals only resemble an ideal model over a small energy range, limiting the optical sources available to study them within a relevant photon energy. Weyl semimetals may also contain trivial bands away from the Weyl fermion structures whose properties can be difficult to disentangle from those of interest./nIn this work, nonlinear optical properties of the Weyl semimetal RhSi are measured. In particular, the theory of the Quantized Circular Photogalvanic Effect [35] in Weyl semimetals, a bulk property, is tested via measurements of terahertz generation and radiation. Precise numerical agreement with the theory is ruled out for the material, although we find the qualitative features of the prediction to hold true. Similar measurements are made in order to examine surface states on RhSi which have proven difficult to experimentally study with traditional transport and optical techniques. The bulk and surface properties are distinguished by considering experimental geometry in concert with crystal symmetry and orientation.

Advisor: Orenstein, Joseph
Evolutionary Insights into Innate Immunity to Bacterial Pathogens

All living organisms have some form of innate immunity, which serves as the first line of defense against pathogen infection. Immune systems are under substantial selective pressure given that pathogens that can evade these defenses will be more evolutionarily successful; therefore studying the evolution of immune systems can inform our knowledge of both host-pathogen interactions and evolutionary forces. Examining immune systems in a variety of extant species can shed light on fundamental properties of innate immunity and also important ways in which mechanisms of immunity diverge under different selective pressures. In this thesis I will explore two innate immune pathways and discuss the evolutionary histories that may have led to their current state./nIn chapter 1, I review the literature on the origin and functions of the cGAS-STING pathway. In mammals, this pathway is crucial for sensing viral infection and initiating an anti-viral interferon response. cGAS and STING are highly conserved genes that originated in bacteria and are present in most animals. By contrast, interferons only emerged in vertebrates; thus, the function of STING in invertebrates was unclear. In chapter 2, I introduce the model cnidarian, Nematostella vectensis, and describe my attempts to examine the ancestral functions of the cGAS-STING pathway. Here, I use the STING ligand 2′3′-cGAMP and RNA-sequencing to probe the consequences of pathway activation, and find a conserved role in immunity via the induction of both anti-viral and anti-bacterial genes. I then perform a series of knockdown experiments and discover a role for the conserved transcription factor NF-κB in specifically inducing anti-bacterial genes downstream of 2′3′-cGAMP. I go on to characterize the protein product of one of the putative anti-bacterial genes and demonstrate its conserved anti-bacterial activity. This work describes an unexpected role of a 2′3′-cGAMP sensing pathway in anti-bacterial immunity, and further implications of this work are discussed in chapter 3./nIn chapter 4, I pivot to a different model system to understand the more recent evolution of the NAIP-NLRC4 inflammasome in mammals. Here I describe redundant outputs downstream of Legionella pneumophila detection by NAIP–NLRC4, either of which is sufficient for pathogen clearance. I speculate that redundancy is an important safeguard against pathogen immune evasion. Overall, this thesis expands our knowledge of how innate immune systems evolve.

Advisor: Vance, Russell E
(Re)Addressing Redress Along the Diagonal: 19th Century African American Women in Missouri

This dissertation argues that in order to better understand the actions and experiences of African American women in nineteenth-century Missouri, we must not only use an intersectional lens that accounts for race and class, but also consider the ways what I call diagonality, which relates to geography and spatial factors, impacted their lives. I use the story of an obscure, formerly enslaved African American woman named Agnes Evans, in Franklin County, to argue that African American women sometimes chose not to pursue redress through legal pathways, and instead redressed their grievances informally, based on a moral authority. Drawing on newspapers, probate cases, and court records, I analyze the case of Evans, who allegedly embezzled personal property from her deceased former enslaver in 1870. I situate this dissertation within studies of the racialized and gendered nature of Black women’s archival silences and invisibility, examining the tension between moral and legal rights, and the ways intersectionality and diagonality shaped decisions around pursuits of redress.

Advisor: Taylor, Ula Y.
Demand Prediction and Capacity Modelling for Applications in En Route Air Traffic Management

In this thesis we cover two aspects of en route air traffic management – airspace demand and air traffic flow. In part 1 we study Random Utility Models of flights’ route choice among available alternatives, the impact of queuing dynamics on the ability to accurately model airlines’ route preferences, tree-based aggregate demand prediction models. We show that random utility models yield lower than previously predicted sensitivity of airlines’ route choice to departure delay assigned by air traffic control authorities. Using mixed logit and latent class choice models we demonstrate that airlines vary significantly by their sensitivity to departure delay. Next, we show that these results can be explained by the ability of airlines to exchange aircraft arrival slots between flights that belong to them. We propose several aggregate methods for estimating the relative cost of flight trajectories that yield better results than discrete choice models. Finally in part 1 of the thesis we develop and test several tree-based machine learning models to predict route choice and aggregate airspace demand. Part 2 of the thesis is devoted to modelling of air traffic flow. First, we build on the existing definition of traffic variables, such as airspace occupancy, air traffic flow and average air traffic speed, to construct new definitions of airspace variables, namely effective flow, effective speed, effective pace, and effective delay. We then use these definitions to develop a simple model that relates these variables together – the Airspace Macroscopic Fundamental Diagram (AMFD) model. We propose ways to estimate the AMFD parameters using linear regression techniques. Finally, we show evidence for the existence of AMFD dynamics in conventional air traffic simulation, observational data of conventional air traffic flow and in simulation of Urban Air Mobility (UAM).

Advisor: Hansen, Mark
Statistical Analysis and Integration of Multi-Modal Sequencing Data

The explosion of high-throughput sequencing technologies in the past decade has enabled the measurement of various molecules and biological processes at the bulk level and the single-cell level, resulting in data modalities such as gene expression, methylation, and chromatin accessibility. Each of these modalities has distinct data properties and therefore poses different statistical challenges. It is important to both study these modalities separately as well as jointly in order to understand their individual characteristics and their complex interactions. This dissertation addresses both questions. For the modeling of a single modality, we first introduce a method for the differential analysis of bulk methylation sequencing data. We propose an approach based on change-point detection that identifies regions in the genome that exhibit different methylation levels between groups or over time. We show that our approach gives improved performance compared to existing methods. We then provide a case study on single-cell gene expression data, illustrating the typical workflow and addressing the challenges involved in analyzing such datasets. For the integration of multiple modalities, we introduce a novel method that focuses on a question that has not yet been addressed: the jointly modeling of single-cell single-modality platforms with multi-modality platforms. We apply the method to integrate a single-cell gene expression dataset, a single-cell chromatin accessibility dataset, and a single-cell joint-platform dataset that sequences both gene expression and chromatin accessibility.

Advisor: Purdom, Elizabeth
“Can You Feel Me…” : The Sociospatial Logics and Ghost-Map of Black and Urban Youth Navigating Urban Violence

This dissertation project is a critical spatial analysis of urban violence. Using a mixed-methods approach to interrogate youth experiences in Oakland, California, this study contributes to intersectional understandings of race, space, and violence, illustrating the significance of sociospatiality, or the (re)productive function of race and space, in the on-going development of urban space, including material and discursive representations of violence across a local urbanscape. The arc of this dissertation is organized around these central questions: (a) How do a group of Black and other youth of color in Oakland experience and understand everyday urban violence in their local geography, across community and school? How do they understand the racial and spatial contours to risk and safety? (b) How does anti-Blackness, and geographies of race generally, function in the creation and maintenance of urban space, including community violence? how can critical spatial analyses deepen our conceptualization and research of urban violence, including youth experiences with everyday precarity? And (c) How can youth narratives and “theories in the flesh” disrupt and counter-map hegemonic representations of urban communities and violence? Thus, the three-article study is organized around two central pillars: on one hand, it reconceptualizes urban violence beyond the interpersonal and spectacularized, attuned to the mutuality of racial-spatial processes and meanings entrenched in place. Moreover, it provides a richer depiction of (a reconceptualized) urban violence through understudied qualitative youth portraits and “theories of the flesh,” which demonstrate sense-making repertoires and safe-keeping. In demonstrating the sociospatiality of race, space, and violence in Oakland, the gravity and capaciousness of an everyday precarity becomes clearer for these young people’s lives. Overall, the dissertation works as a justice-centered advocacy project to counter-map understandings of urban violence, and asks youth practitioners, policy-makers, and researchers to feel the gravity of their stories and lessons.

Advisor: Gutiérrez, Kris D.
Nasir, Na'ilah Suad
Higher Dimensional Trichotomy Conjectures in Model Theory

In this thesis we study the Restricted Trichotomy Conjectures for algebraically closed and o-minimal fields. These conjectures predict a classification of all sufficiently complex, that is, non-locally modular, strongly minimal structures which can be interpreted from such fields. Such problems have been historically divided into `lower dimensional' and `higher dimensional' cases; this thesis is devoted to a number of partial results in the higher dimensional cases. In particular, in ACF_0 and over o-minimal fields, we prove that all higher dimensional strongly minimal structures whose definable sets satisfy certain geometric restrictions are locally modular. We also make progress toward verifying these geometric restrictions in any counterexample. Finally, in the last chapter we give a full proof of local modularity for strongly minimal expansions of higher dimensional groups in ACF_0.

Advisor: Scanlon, Thomas
Learning, Planning, and Acting with Models

While the classical approach to planning and control has enabled robots to achieve various challenging control tasks, it requires domain experts to specify transition dynamics as well as inferring hand-designed symbolic states from raw observations. Therefore, bringing such method into a diverse, unstructured environment is still a grand challenge. Recent successes in computer vision and natural language processing have shed light on how robot learning could be pivotal in tackling such complexity. However, there are many challenges in deploy learning-based systems such as (1) data efficiency -- how to minimize the amount of training data required, (2) generalization -- how to handle tasks that the robots are not explicitly trained on, and (3) long-horizon tasks -- how to simplify the optimization complexity when presented with temporal-extended tasks./nIn this thesis, we present learning-and-planning methods that utilize deep neural networks to model the environments in different forms in order to facilitate planning and acting. We validate the efficacy of our methods in data efficiency, generalization, and long-horizon tasks on simulated locomotion benchmarks, navigation tasks, and real robot manipulation tasks.

Advisor: Abbeel, Pieter PA
Russell, Stuart SR
Coefficient Optimal Control Problems for Elliptic PDE

In this thesis, we consider a class of optimal control problems known as coefficient control problems.Such problems are constrained by uniformly elliptic PDE in which the controls appear as some of the coefficients in the differential operator. We begin with a brief review of standard optimal control theory and show that it does not apply to control coefficient problems generally by means of two counterexamples. We then present existence results for the solution to such problems under two different assumptions regarding the controls: Lipschitz continuity with a bounded Lipschitz constant and the case in which the admissible set of controls is closed under H-convergence. We then present a new maximum principle for the latter class of problems which we subsequently use to characterize the nature and behavior of optimal solutions.

Advisor: Evans, Lawrence C