VictorJuiz/thesis huggingface dataset:
A complexity-theoretic perspective on fairness

Algorithms make predictions about people constantly. The spread of such prediction systems---from precision medicine to targeted advertising to predictive policing---has raised concerns that algorithms may perpetrate unfair discrimination, especially against individuals from minority groups. While it's easy to speculate on the risks of unfair prediction, devising an effective definition of algorithmic fairness is challenging. Most existing definitions tend toward one of two extremes---individual fairness notions provide theoretically-appealing protections but present practical challenges at scale, whereas group fairness notions are tractable but offer marginal protections. In this thesis, we propose and study a new notion---multi-calibration---that strengthens the guarantees of group fairness while avoiding the obstacles associated with individual fairness. Multi-calibration requires that predictions be well-calibrated, not simply on the population as a whole but simultaneously over a rich collection of subpopulations C. We specify this collection---which parameterizes the strength of the multi-calibration guarantee---in terms of a class of computationally-bounded functions. Multi-calibration protects every subpopulation that can be identified within the chosen computational bound. Despite such a demanding requirement, we show a generic reduction from learning a multi-calibrated predictor to (agnostic) learning over the chosen class C. This reduction establishes the feasibility of multi-calibration: taking C to be a learnable class, we can achieve multi-calibration efficiently (both statistically and computationally). To better understand the requirement of multi-calibration, we turn our attention from fair prediction to fair ranking. We establish an equivalence between a semantic notion of domination-compatibility in rankings and the technical notion of multi-calibration in predictors---while conceived from different vantage points, these concepts encode the same notion of evidence-based fairness. This alternative characterization illustrates how multi-calibration affords qualitatively different protections than standard group notions

Advisor: Reingold Omer
Valiant Gregory
Zou James
Composing social dynamics

The performance-installation one to one (2018-20) presents music as a dynamic, social interaction--a site to forge connections via entrainment. It is an intimately staged sequence of three pieces, each for one performance and one audience member at a time. Interpersonal entrainment describes how human beings mutually adapt to and coordinate with one another over time. These processes facilitate pro-social effects across a variety of activities, including music making. In one to one (2018-20), I contribute novel resources (i.e. conceptual frameworks, situative scoring strategies, heuristics) for creative practice, means of composing social dynamics, which nuance and expand the field of performer-audience interactions. Nonverbal communication studies offer the (quasi-)periodic cues performers attend to in each audience member. Interpersonal entrainment describes the dyadic micro-dynamics that unfold between them--reframing musical rhythm in social terms. By reciprocating each audience member's attentional and behavioral rhythms in a sensitive, co-operative manner, the performers offer exercises in empathetic imagination

Keywords: Music.
Advisor: Ferneyhough Brian
Berger Jonathan
Kapuscinski Jaroslaw
Ulman Erik
Computational and empirical exploration of the interaction between semantics and pragmatics in vague language

Some language is straightforward to interpret, regardless of the context. Other language is more vague, and requires a context in order to reach a concrete interpretation. This work is focused on formal models of vagueness in language, which requires a joint inference about the person's intention (i.e. what information they're trying to convey) and the meanings of the words they are using in this context. In this work, we present several empirical and model explorations of how people use vague language. We provide experimental exploration of how the particular prior expectations and numeric amounts in sorites paradox lead to different endorsements of the premises. We show how a simple extension of an RSA scalar adjectives model quantitatively predicts endorsement levels for sorites premises. We further modify this adjective model to account for intensifying degree adverbs, and demonstrate a surprising prediction of this model: that the form and meaning of intensifying degree adverbs are closely related, with intensifier degree proportional to utterance cost (where cost can be approximated by length of phrase or in inverse frequency). Finally, we apply principles of pragmatic reasoning to the meanings of different periphrastic causal expressions and compare the predictions of two pragmatic models to participants forced choice responses. Much of language is inherently vague, and so in order to communicate, people must reason over uncertainty about the particular meanings of their words in context. This leads to surprising inferences and relationships between utterances under standard logical approaches to semantics, which can be explained by pragmatic principles of communication

Keywords: Gerstenberg, Tobias, degree supervisor
Psychology.
Advisor: Goodman Noah (Noah D.)
Frank Michael C. (Professor of human biology)
Computational and statistical theories for large-scale neural networks

Deep learning methods operate in regimes that defy the traditional computational and statistical mindsets. Despite the non-convexity of empirical risks and the huge complexity of neural network architectures, stochastic gradient algorithms can often find an approximate global minimizer of the training loss and achieve small generalization error on test data. In recent years, an important research direction is to theoretically explain these observed optimization efficiency and generalization efficacy of neural network systems. This thesis tries to tackle these challenges in the model of two-layers neural networks, by analyzing its computational and statistical properties in various scaling limits. On the computational aspects, we introduce two competing theories for neural network dynamics: the mean field theory and the tangent kernel theory. These two theories characterize training dynamics of neural networks in different regimes that exhibit different behaviors. In the mean field framework, the training dynamics, in the large neuron limit, is captured by a particular non-linear partial differential equation. This characterization allows us to prove global convergence of the dynamics in certain scenarios. Comparatively, the tangent kernel theory characterizes the same dynamics in a different scaling limit and provides global convergence guarantees in more general scenarios. On the statistical aspects, we study the generalization properties of neural networks trained in the two regimes as described above. We first show that, in the high dimensional limit, neural tangent kernels are no better than polynomial regression, while neural networks trained in the mean field regime can potentially perform better. Next, we study more carefully the random features model, which is equivalent to a two-layers neural network in the kernel regime. We compute the precise asymptotics of its test error in the high dimensional limit and confirm that it exhibits an interesting double-descent curve that was observed in experiments

Advisor: Montanari Andrea
Johnstone Iain
Ying Lexing
Computational approaches to the history and structure of education research

The goal of this project is to examine the development and current state of U.S. education research through the lens of computational social science. The dissertation comprises three studies which use distinct data—dissertations, library logs, patents—and methods—topic models, navigational networks, semantic networks—to approach the history and structure of education research

Keywords: Education.
Advisor: McFarland Daniel (Daniel A.) 
Willinsky John
Goldberg Amir
Computational flame propagation studies in support of launch vehicle risk assessment

Understanding how flames propagate in a failure scenario and the associated risk is important to many different fields, including hydrogen storage facilities, mines, and other relatively confined industrial settings, but the focus here is launch vehicles. Large, uncontained, explosive failures occurring in the engine bay of space launch vehicles and resulting from the unwanted release, mixture, and ignition of a fuel source, pose a significant risk to the crew, surrounding personnel, and equipment. Explosions in these failures involve complex physical phenomena and the flame propagation can be extremely sensitive to specific conditions at the time of ignition, for example, confinement and congestion. The combination of complex physics, potentially complicated geometries, and flame sensitivities make blast environments very difficult to characterize. There are three main options for characterizing the environment for use in risk assessments, including physical experiments, computational fluid dynamics (CFD) simulations, and engineering-level blast models. Low cost (time, money, resources) engineering models are the most practical option for a probabilistic approach, but insight from experiments and CFD studies are needed to inform and develop the models. The goal of this work is to use CFD simulations to better understand various flame propagation mechanisms and sensitivities to different parameters. The results will be used to support the development of physics-based probabilistic risk assessment models leading to guidelines for designing safer launch vehicles. The work was broken into two main computational studies, each with several substudies analyzing the effects of varying different parameters. The first study focused on mechanisms of deflagration to detonation transition in confined, obstructed flows, and found that scenarios with limited non-bluff obstacles, limited points of ignition, and lower initial pressure, density, and temperature reduced the risk of detonation initiation because these factors all reduced the potential for large flame accelerations. The second study focused on the influence of underlying flow parameters on deflagration flame propagation, and found that characterizing the underlying flow is important because scenarios with features in the flow tend to cause local flame deformations and acceleration, increasing flame speed and overpressure and changing the potential risk. Parameters like vorticity magnitude and invariants of the velocity gradient tensor may be effective indicators in the model for when local acceleration is likely to take place. All of the approximately 35 cases considered between the two studies were chosen to represent possible scenarios in rocket engine bays, and the insight developed will lead to improved launch vehicle risk assessments

Keywords: Aeronautics and Astronautics.
Advisor: Cantwell Brian 
Bowman Craig T. (Craig Thomas)
Farhat Charbel
Mathias Donovan L.
A computational framework for learning and transforming task representations

Human cognition is fundamentally flexible — we can adapt to novel tasks rapidly. We can sometimes adapt to a novel task without any direct experience on that task, based on its relationship to previous tasks. By contrast, while deep-learning models can achieve superhuman performance on many tasks, they are often unable to adapt to even slight task alterations. This ostensible inflexibility has led to criticism of deep learning models by cognitive scientists. I begin this dissertation by reviewing the literature on cognitive flexibility, and recent advances in building more flexible artificial intelligence systems. I provide a synthesis of these literatures, and outline the challenges that I believe remain. In particular, I focus on the ability to adapt to new tasks zero-shot — that is, without any data — based on their relationship to prior tasks. To address this challenge, I propose a general computational framework for adaptation to novel tasks based on their relationship to prior tasks. The framework is based on meta-mappings, higher-order tasks that transform basic tasks. I propose a parsimonious implementation of this framework in the form of homoiconic meta-mapping architectures. I demonstrate this framework across a wide variety of tasks and computational paradigms, ranging from regression to image classification and reinforcement learning. I compare to both human adaptability, and language-based approaches to zero-shot task performance. I show that meta-mapping is quite succesful, often achieveing 80-90% performance on a novel task, even when the new task directly contradicts prior experience. I further show that using this adaptation as a starting point can dramatically accelerate later learning on a task, and reduce the errors made on the way to mastery by nearly an order of magnitude. Thus, I suggest that meta-mapping can provide a computational basis for adapting to new tasks, and a starting point for efficient learning. This dissertation therefore provides a framework for building better cognitive models and more flexible artificial intelligence systems. In the final chapter, I review the broader contributions of this work to an ongoing discussion about the computational principles necessary for intelligence, and highlight possible future directions ranging from understanding mathematical cognition to neuroscience

Keywords: Psychology.
Advisor: McClelland James L. 
Ganguli Surya
Goodman Noah (Noah D.)
Computational imaging with single-photon detectors

Active 3D imaging systems, such as LIDAR, are becoming increasingly prevalent for applications in human-computer interaction, robotics, autonomous vehicle navigation, remote sensing, and more. However, the ability to image with short acquisition times or at long distances is fundamentally limited by the weak signal of backscattered light. Moreover, conventional active imaging systems fail to exploit information contained in multiply-scattered light. In this thesis, we develop novel computational imaging frameworks for photon-efficient 3D imaging and imaging with multiply scattered light using time-resolved detectors that are sensitive to single photons. Our framework for photon-efficient 3D imaging uses convolutional neural networks to jointly process measurements from a single-photon detector and a conventional camera. The approach outperforms existing techniques in a challenging regime where the average number of detected signal photons at each pixel is less than one. Our frameworks for imaging with multiply scattered light efficiently model and invert light transport, enabling new capabilities in non-line-of-sight imaging (i.e., imaging around corners or behind occluders) and imaging through scattering media. For example, we demonstrate significant improvements to the computational efficiency and accuracy of non-line-of-sight imaging, enabling reconstruction of large hidden scenes with unprecedented resolution and fidelity. Our method for 3D imaging through scattering media efficiently models light propagation through a diffusive barrier and outperforms techniques like time- or coherence-gating

Keywords: Electrical Engineering.
Advisor: Wetzstein Gordon 
Girod Bernd
Horowitz Mark (Mark Alan)
Computational nanophotonic design : frameworks and applications

In photonic inverse design, an automated procedure is used to search through designs that may have unintuitive shapes previously unconsidered by photonic designers. In recent years, the inverse design method has achieved more compact, efficient, and robust devices as compared to traditional photonic devices. However, inverse design has yet to become a standard, mainstream design technique. In this dissertation, we address several of the challenges in transforming inverse design into a practical design tool. First, inverse design devices are typically small in size because of the computational costs associated with rigorously simulating large electromagnetic structures. Here, we demonstrate a fast and accurate simulation for metasurfaces that are hundreds to thousands of wavelengths in size. Second, inverse design devices may exhibit geometries that are difficult to fabricate. Here, we show that appropriately fabrication-constrained inverse-designed devices can be successfully fabricated in commercial foundries. Finally, we develop SPINS, a nanophotonic inverse design framework that emphasizes flexibility and reproducibility. There are four main elements to the inverse design method: structure parametrization, the electromagnetic simulator, the objective function, and the optimization procedure. SPINS enables the designer to easily choose the appropriate elements to use for their particular photonic design problem, thus enabling the inverse design method to be more readily applicable to a larger number of design problems

Keywords: Applied Physics.
Advisor: Vuckovic Jelena
Miller D. A. B. 
Safavi-Naeini Amir H.
Computational neuroengineering for artificial retina

A major goal of neuroengineering is to control spiking activity at cellular resolution in a way that replicates neural code. Our context is the artificial retina, a neural interface that aims to treat blindness due to retinal degeneration by interfacing with remaining retinal ganglion cells. Using primate retina recordings from a large-scale multielectrode array as a lab prototype of the device, we study three important problems : (1) How does the retina normally respond to visual stimuli? (2) How do we use healthy retinas to predict responses in a blind retina, in spite of individual variability? (3) How can we replicate this response pattern by electrical stimulation? First, we build a model for visual encoding that incorporates non-linear spatial integration, a prevalent computational motif in neural circuits. We identify subunits that hierarchically partition the receptive field into localized components. For prosthesis application, we extend the method to find shared nonlinear substrates in cell populations and describe their role in explaining responses to natural stimulus. Second, for translating these models to restoring visual function in a blind retina, we study the variation in visual encoding across a hundred recordings from different animals. We learn a low dimensional manifold to separate individual variability from shared computations. Using the manifold, we study the covariation of response properties of different cell-types and observe surprising differences in neural encoding between males and females. The low dimensional manifold also helps in efficiently identifying the neural code in a previously unseen retina, and calibrating device function for a bilnd retina. Finally, we present an approach to replicate neural responses as accurately as possible when the interface has imperfect selectivity. By temporally multiplexing a collection of non-selective patterns, we show improved performance compared to the algorithms used by existing devices. Under simplifying assumptions, a greedy algorithm for identifying the sequence of stimulation is surprisingly close to optimal. This framework allows us to use experimental data to derive insights on the hardware design

Keywords: Electrical Engineering.
Advisor: Chichilnisky E. J.
Boyd Stephen P. 
Mitra Subhasish
Computational Optimization of Solar Thermal Energy Generation and Storage

Integrating renewable energy resources into the power generation system is essential for achieving future renewable portfolio standards. Solar thermal is not as popular as solar photovoltaic, but it does enable thermal energy storage (TES), which can provide longer durations of storage than many other technologies. The expense of installation is a significant drawback of solar thermal with TES, but the cost is declining over time. Permitting these operators to sell power at retail utility rates may improve the economics of solar thermal projects.
In this work, we model and optimize a 15 MWe parabolic trough concentrating solar power plant with thermal energy storage. The model simulates power output of a 10 hectare solar field assumed to operate in Daggett, California. We use a proxy model for the solar field, and simulate a three-stage heat exchange process, a thermal energy storage tank with radiative, convective, and conductive heat loss, and a steam turbine. Irradiation data from the National Solar Radiation Database (NSRDB) [4] and electricity price data from the California Independent System Operator (CAISO) [1] are clustered into representative days for the year 2017. We use the Particle Swarm Optimization (PSO) - Mesh Adaptive Direct Search (MADS) hybrid optimization scheme to solve for the optimal operations of the plant. Decision variables include the mass flow rates into and out of storage at hours 7-22 for each clustered day. Constraints include the pinch temperature, approach temperature, steam quality, and temperature ranges of the heat transfer fluid (HTF).
We first consider idealized price curves, for which the optimal operations are intuitive, and observe that optimizer performance agrees with intuition for these cases. We then use the CAISO price data and evaluate the value of two-hour, four-hour, and six-hour storage tanks by optimizing operations over each clustered day to compute the annual profit and net present value (NPV) of a plant over 30 years. We find that the four-hour storage tank is optimal, while the high installation cost of the six-hour storage tank renders it suboptimal despite a higher annual operating profit. Lastly, we conduct a sensitivity analysis to compare the base case, a “fatter” duck price curve, and Power Purchase Agreements (PPAs). We find that a six-hour storage tank yields the highest NPV with the more volatile prices of the “fatter” duck curve, as the higher peaks in electricity prices offset the higher cost of a six-hour storage tank. A plant operating under a PPA without storage is found to be the least profitable, suggesting that it is beneficial to utilize TES and sell at market prices rather than enter into a PPA.

Keywords: Solar thermal energy storage
energy systems optimization
Thesis
Advisor: Durlofsky Louis
Brandt Adam