Registration & talk details

This talk is one of Data Science Applied Research and Education Seminar (ARES) series. Learn more and register for this talk here.

Talk Title: Understanding animal movement with state-space models

Abstract: Movement data have become essential for our understanding of animal ecology. However, such data are associated with a broad range of challenges. For example, tracking the movement of many species (e.g. fish and small birds) is still limited to inaccurate technology, such as light-based geolocation. Making behavioral and spatial inferences based on such data is difficult because the tracks they create are not good representations of the animals’ movement. Understanding the behavior of animals using movement data is challenging even with accurate movement data, because we are often making inference on a process that is not observed directly. Through a set of examples from a broad range of animals (fish, bears, marine mammals), I will demonstrate how a state-space modeling framework can improve our understanding of animal movement.

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Abstract: MCMC methods are a popular tool in computation science used to evaluate expectations with respect to complex probability distributions over general state spaces. They work by averaging over the trajectory of a Markov chain stationary with respect to the target distribution. In theory, the MCMC algorithms converge asymptotically, but, in practice, for challenging problems where the target distributions are high-dimensional with well-separated modes, MCMC algorithms can get trapped exploring local regions of high probability and suffer from poor mixing.

Physicists and statisticians independently introduced parallel tempering (PT) algorithms to tackle this issue. PT delegates the task of exploration to additional annealed chains running in parallel with better mixing properties. They then communicate with the target chain of interest and help discover new unexplored regions of the sample space. Since their introduction in the 90s, PT algorithms are still extensively used to improve mixing in challenging sampling problems arising in statistics, physics, computational chemistry, phylogenetics, and machine learning.

The classical approach to designing PT algorithms was developed using a reversible paradigm that is difficult to tune and deteriorates in performance when too many parallel chains are introduced. This talk will introduce a new non-reversible paradigm for PT that dominates its reversible counterpart while avoiding the performance collapse endemic to reversible methods. We will then establish near-optimal tuning guidelines and efficient black-box methodology scalable to GPUs. Our work out-performs state-of-the-art PT methods and has been used at scale by researchers to study the evolutionary structure of cancer and discover magnetic polarization in the photograph of the supermassive black hole M87.

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Title: Doctor for AI: Prior-informed ML for Biomedical Imaging and Perception

Abstract: Deepening our understanding of human health is more important than ever before to address real-world challenges in biomedicine and healthcare, especially with the pandemic over recent years. My research focuses on AI in medicine, to develop efficient ML models for biomedical imaging and perception for addressing real-world challenges. In this talk, I will first explore the challenges in this emerging field and then present the two following lines of my work:

First, I will introduce my work in Doctor for AI: leverage the prior knowledge of doctors to design AI model for biomedical imaging. Specifically, I will discuss how to integrate different kinds of prior knowledge to develop reliable data-efficient ML models, by exploiting the personalized prior, population prior and physics prior. With the innovative ML models, the proposed approaches can be generally applied to various biomedical imaging applications including sparse-sampling image reconstruction, projection synthesis, and molecular imaging.

Second, I will introduce my work in AI for Doctor: develop ML-driven perception models that can adaptive to unique characteristics of biomedical data including random noise and multi-modality. Specifically, I will present a self-attention-guided ML model for quantitative image perception. Through international collaborations for cross-institute validation among four U.S. clinical centers and a Turkey institute, this work demonstrates the possibility for the developed ML model to characterize the in utero neurodevelopmental trajectory in real-world deployment.

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Title: What can statisticians learn from the analysis of C.elegans data?

Abstract: In modern scientific setups we are faced with unprecedented challenges regarding how to process data efficiently and in a robust way. These challenges often reveal the brittleness of our current tools, dictating the need for new methods. In this talk I will describe new statistical and AI methods motivated by a pressing problem in neuroscience, the need for imaging entire brains at single-neuron resolution.

Specifically, I will present my contribution to NeuroPAL, a new breakthrough technology that enables a colorful imaging of every single neuron in the brain of the C.elegans worm. I will describe new methods for two difficult tasks arising in these datasets: neural segmentation and identification. These two tasks are related to an underlying deconvolution model, a mixture of gaussians, and classical methods such as the EM algorithm fall short. Behind these new methods there is a key statistical physics principle, the so-called Schrödinger bridge, a ‘thought experiment’ that realizes the solution of an entropy-regularized optimal transport problem. This thought experiment was proposed in 1932 but it has yet to percolate into the mainstream of statistics.

I will first describe some fundamental statistical properties of the Schrödinger bridge that I established. For example, when estimating it from samples, it enjoys the 1/sqrt n convergence rate, avoiding the curse of dimensionality. Second, I will introduce a new loss function based on this principle and show that it is a better optimization objective than the log-likelihood for model-based clustering, reducing pathologies such as bad local optima and inconsistency. In consequence, a new algorithm derived from this loss, Sinkhorn EM, attains better, more robust neural segmentation performance. After, I will comment on how these principles can be used to probabilistically identify neurons in C.elegans, leading to meaningful uncertainty quantification on this hard combinatorial setup. Finally, I will comment on how these novel methods have proven to be useful in other contexts such as deep learning.

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Title: Bridging the Gap Between Deep Learning and Probabilistic Modeling

Abstract: Deep learning excels with large-scale unstructured data - common across many modern application domains - while probabilistic modeling offers the ability to encode prior knowledge and quantify uncertainty - necessary for safety-critical applications and downstream decision-making tasks. I will discuss examples from my research that bridge the gap between these two learning paradigms. The first half will show that insights from deep learning can improve the practicality of probabilistic models. I will discuss work that scales Gaussian process regression, a common probabilistic model, to datasets two orders of magnitude larger than previously reported. The second half will show that probabilistic methods can improve our understanding of deep learning. I will demonstrate that Gaussian process theory uncovers new insights about the effects of width and depth in neural networks. I will conclude with ongoing efforts to quantify neural network uncertainty, develop new inductive biases, and other work at the intersection of deep learning and probabilistic modeling.

Registration

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Abstract

One hopes that data analyses will be used to make beneficial decisions regarding people's health, finances, and well-being. But the data fed to an analysis may systematically differ from the data where these decisions are ultimately applied. For instance, suppose we analyze data in one country and conclude that microcredit is effective at alleviating poverty; based on this analysis, we decide to distribute microcredit in other locations and in future years. We might then ask: can we trust our conclusion to apply under new conditions? If we found that a very small percentage of the original data was instrumental in determining the original conclusion, we might expect the conclusion to be unstable under new conditions. So we propose a method to assess the sensitivity of data analyses to the removal of a very small fraction of the data set. Analyzing all possible data subsets of a certain size is computationally prohibitive, so we provide an approximation. We call our resulting method the Approximate Maximum Influence Perturbation. Our approximation is automatically computable, theoretically supported, and works for common estimators – including (but not limited to) OLS, IV, GMM, MLE, MAP, and variational Bayes. We show that any non-robustness our metric finds is conclusive. Empirics demonstrate that while some applications are robust, in others the sign of a treatment effect can be changed by dropping less than 0.1% of the data – even in simple models and even when standard errors are small.

van Eeden speakers

Professor Tamara Broderick has been invited by our department's graduate students to be this year's van Eeden speaker. A van Eeden speaker is a prominent statistician who is chosen by our graduate students each year to give a lecture, supported by the Constance van Eeden Fund.

To join via Zoom: To join this seminar, please request Zoom connection details from pims@uvic.ca  

Title: Meta-Analytic Inference for the COVID-19 Infection Fatality Rate

Abstract: Estimating the COVID-19 infection fatality rate (IFR) has proven to be challenging, since data on deaths and data on the number of infections are subject to various biases. I will describe some joint work with Harlan Campbell and others on both methodological and applied aspects of meeting this challenge, in a meta-analytic framework of combining data from different populations. I will start with the easier case when the infection data are obtained via random sampling. Then I will discuss drawing in additional infection data obtained in decidedly non-random manner.

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Title: Iterated Block Particle Filter for High-dimensional Parameter Learning: Beating the Curse of Dimensionality

Abstract: Parameter learning for high-dimensional, partially observed, and nonlinear stochastic processes is a methodological challenge. Spatiotemporal disease transmission systems provide examples of such processes giving rise to open inference problems. We propose the iterated block particle filter (IBPF) algorithm for learning high-dimensional parameters over graphical state space models with general state spaces, measures, transition densities and graph structure. Theoretical performance guarantees are obtained on beating the curse of dimensionality (COD), algorithm convergence, and likelihood maximization. Experiments on a highly nonlinear and non-Gaussian spatiotemporal model for measles transmission reveal that the iterated ensemble Kalman filter algorithm (Li et al. (2020), Science) is ineffective and the iterated filtering algorithm (Ionides et al. (2015), PNAS) suffers from the COD, while our IBPF algorithm beats COD consistently across various experiments with different metrics.

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Title: Instance-dependent Reinforcement Learning: A statistical viewpoint

Abstract: In recent years, there has been tremendous progress in the field of reinforcement learning (RL), especially on the empirical side. But it is fair to say that there is a considerable gap between theory and practice: many RL methods behave far better than existing worst-case theory would suggest, and often they work in settings where the current worst-case guarantees are completely prohibitive. In this talk, we will discuss why worst-case guarantees can severely overestimate the difficulty of reinforcement learning problems in presence of favorable structure. This motivates us to consider an instance-dependent difficulty measure that is responsive to the problem structure. Next, we discuss how we can construct estimators that adapt to this instance-dependent difficulty. We show that for problems with favorable structures our proposed estimators and associated confidence regions are significantly better than those obtained from the worst-case theory. Finally, we show that the techniques that we developed for constructing instance-dependent estimators are not specific to RL problems, and can be applied to a broad class of other problems.

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Dear STAT news subscribers:

Please be advised that this seminar has been cancelled. We will reschedule the seminar in the near future. We sincerely apologize for any inconvenience.

Best wishes,

UBC Statistics Department

Registration & talk details

This talk has been organized by the Canadian Statistical Sciences Institute (CANSSI) Saskatchewan Health Science Collaborating Centre (HSCC). Learn more and register for this talk here.

Talk Title: Gaussian Mixture Reduction based on Composite Transportation Divergence

Abstract: In many applications, researchers wish to approximate a finite Gaussian mixture distribution with a high order by one with a lower order. Examples include density estimation, recursive tracking in hidden Markov model, and belief propagation. A direct solution to such a Gaussian Mixture Reduction problem is computationally challenging due to the non-convexity of commonly employed optimality targets.

One popular line of approach is to employ some clustering-based iterative algorithms. Neither their convergence nor destination, however, are thoroughly discussed. In this paper, we propose a new GMR method by minimizing some novel composite transportation divergence (CTD). This divergence permits an easy to implement Majorization-Minimization (MM) algorithm. We prove that the MM algorithms converge under general conditions, and many existing clustering-based algorithms are special cases of our approach. We further investigate the property of this approach with various choices of cost functions and demonstrate its effectiveness and computational costs.