FPO

Diana Cai will present her FPO "Probabilistic Inference When the Model is Wrong" on Wednesday, July 5, 2023 at 3:00 PM in COS 105.

Location: COS 105

The members of Diana’s committee are as follows:
Examiners: Ryan Adams (Adviser), Barbara Engelhardt (Adviser), Olga Russakovsky
Readers: Tom Griffiths, Tamara Broderick (MIT)

A copy of her thesis is available upon request. Please email gradinfo@cs.princeton.edu if you would like a copy of the thesis.

Everyone is invited to attend her talk.

Abstract follows below:
By simplifying complex real-world phenomena, probabilistic methods have proven able to accelerate applications in discovery and design. However, classical theory often evaluates models under the assumption that they are perfect representations of the observed data. There remains the danger that these simplifications might sometimes lead to failure under real-world conditions. This dissertation identifies popular data analyses that can yield unreliable conclusions—and in some cases ones that are arbitrarily unreliable—under such “misspecification.” But we also show how to practically navigate misspecification. To begin, we consider clustering, a mainstay of modern unsupervised data analysis, using Bayesian finite mixture models. Some scientists are interested not only in finding meaningful groups of data but also in learning the number of such clusters. We provide novel theoretical results and empirical studies showing that, no matter how small the misspecification, some common approaches, including Bayesian robustness procedures, for learning the number of clusters give increasingly wrong answers as one receives more data. But using imperfect models need not be hopeless. For instance, we consider a popular Bayesian modeling framework for graphs based on the assumption of vertex exchangeability. A consequence of this assumption is that the resulting graph models generate dense graphs with probability 1 and are therefore misspecified for sparse graphs, a common property of many real-world graphs. To address this undesirable scaling behavior, we introduce an alternative generative modeling framework and prove that it generates a range of sparse and dense scaling behaviors; we also show empirically that it can generate graphs with sparse power law scaling behavior. Finally, we consider the case where a researcher has access to a sequence of approximate models that become arbitrarily more complex at the cost of more computation, which is common in applications with simulators of physical dynamics or models requiring numerical approximations of some fidelity. In this case, we show how to obtain estimates as though one had access to the most complex model. In particular, we propose a framework for constructing Markov chain Monte Carlo algorithms that asymptotically simulates from the most complex model while only ever evaluating models from the sequence of approximate models.

Ruairidh Battleday will present his FPO "The Role Of Nonparametric Inference In Computational Models Of Categorization And Analogy" on Thursday, June 29, 2023 at 1:00 PM in COS 401 and Zoom.

Location: Zoom link: https://princeton.zoom.us/j/91969001576

The members of Ruairidh’s committee are as follows:
Examiners: Tom Griffiths (Adviser), Ryan Adams, Barbara Engelhardt
Readers: Olga Russakovsky, Tania Lombrozo

A copy of his thesis is available upon request.  Please email gradinfo@cs.princeton.edu if you would like a copy of the thesis.

Everyone is invited to attend his talk.

Abstract follows below:

This dissertation presents a computational investigation into the nature of human learning and inference. Its central thesis is that humans make good predictions in novel environments because they possess flexible abilities for inductive inference that can be used to generalize and update relevant knowledge abstracted from the past. This thesis is first explored in the context of natural image categorization. Here, participants’ judgments are best captured by models that use probabilistic strategies to relate novel stimuli to existing category members, and techniques from deep learning to represent these stimuli in high-dimensional mathematical spaces. The second context is generalization, where the underlying structure of different training games is shown to affect participants’ predictive generalizations on a final test game. These behaviors are accounted for by a nonparametric Bayesian model that aggregates mathematical abstractions of particular training environments, and then weights their predictions about unobserved interactions by their analogical relevance. Common to both lines of inquiry is the idea of using probabilistic models to account for flexible inferential behaviors, and tools from statistics and machine learning to abstract relevant mathematical representations from past experiences or stimuli.

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Title: Quantum and Quantum-Inspired Computation for Wireless Networks.

Examiners: 

Prof. Kyle Jamieson (CS, advisor): kylej (@cs.princeton.edu)

Prof. Jennifer Rexford (CS): jrex (@cs.princeton.edu)

Prof. Yasaman Ghasempour (ECE): ghasempour (@princeton.edu)
 

Readers: 

Prof. Ravi Netravali (CS): rnetravali (@cs.princeton.edu)

Prof. Lin Zhong (Yale): lin.zhong (@yale.edu)

Dr. Davide Venturelli (NASA/USRA RIACS): DVenturelli (@usra.edu)

Vikram Ramaswamy will present his FPO "Tackling bias within Computer Vision Models" on Monday, May 8, 2023 at 12:00 PM in CS 402.

Location: CS 402

The members of Vikram’s committee are as follows: Examiners: Olga Russakovsky (Adviser), Ellen Zhong, Ryan Adams
Readers: Jia Deng, Andrés Monroy-Hernández

A copy of his thesis is available upon request.  Please email gradinfo (@cs.princeton.edu) if you would like a copy of the thesis.

Everyone is invited to attend his talk.

Abstract follows below:
Over the past decade the rapid increase in the ability of computer vision models has led to their applications in a variety of real-world applications from self-driving cars to medical diagnoses. However, there is increasing concern about the fairness and transparency of these models. In this thesis, we tackle these issue of bias within these models along two different axes. First, we consider the datasets that these models are trained on. We use two different methods to create a more balanced training dataset. First, we create a synthetic balanced dataset by sampling strategically from the latent space of a generative network. Next, we explore the potential of creating a dataset through a method other than scraping the internet: we solicit images from workers around the world, creating a dataset that is balanced across different geographical regions. Both techniques are shown to help create models with less bias. Second, we consider methods to improve interpretability of these models, which can then reveal potential biases within the model. We investigate a class of interpretability methods called concept-based methods that output explanations for models in terms of human understandable semantic concepts. We demonstrate the need for more careful development of the datasets used to learn the explanation as well as the concepts used within these explanations. We construct a new method that allows for users to select a trade-off between the understandability and faithfulness of the explanation. Finally, we discuss how methods that completely explain a model can be developed, and provide heuristics for the same.

Yuting Yang will present her FPO "Exploiting Program Representation with Shader Applications" on Wednesday, May 3, 2023 at 3:00 PM in CS 402.

Location: CS 402

The members of Yuting’s committee are as follows:
Examiners: Adam Finkelstein (Adviser), Szymon Rusinkiewicz, Jia Deng
Readers: Felix Heide, Connelly Barnes (Adobe Research)

A copy of her thesis will be available upon request, two weeks before the FPO.  Please email gradinfo (@cs.princeton.edu) if you would like a copy of the thesis.

Everyone is invited to attend her talk.

Abstract follows below:
Programs are widely used in content creation. For example, artists design shader programs to procedurally render scenes and textures, while musicians construct “synth” programs to generate electronic sound. While the generated content is typically the focus of attention, the programs themselves offer hidden potential for transformations that can support untapped applications. In this dissertation, we will discuss four projects that exploit the program structure to automatically apply machine learning or math transformations as if they were manually designed by domain experts. First, we describe a compiler-based framework with novel math rules to extend reverse mode automatic differentiation so as to provide accurate gradients for arbitrary discontinuous programs. The differentiation framework allows us to optimize procedural shader parameters to match target images. Second, we extend the differentiation framework to audio “synth” programs so as to match the acoustic properties of a provided sound clip. We next propose a compiler framework to automatically approximate the convolution of an arbitrary program with a Gaussian kernel in order to smooth the program for visual antialiasing. Finally, we explore the benefit of program representation in deep-learning tasks by proposing to learn from program traces of procedural fragment shaders – programs that generate images. In each of these settings, we demonstrate the benefit of exploiting the program structure to generalize hand-crafted techniques to arbitrary programs.

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