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Machine Learning

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Event Series Special Seminar Series

Acceleration in Optimization, Sampling, and Machine Learning

SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United States

Optimization, sampling, and machine learning are essential components of data science. In this talk, I will cover my work on accelerated methods in these fields and highlight some connections between them.

In optimization, I will present optimization as a two-player zero-sum game, which is a modular approach for designing and analyzing convex optimization algorithms by pitting a pair of no-regret learning strategies against each other. This approach not only recovers several existing algorithms but also gives rise to new ones. I will also discuss the use of Heavy Ball in non-convex optimization, which is a popular momentum method in deep learning. Despite its success in practice, Heavy Ball currently lacks theoretical evidence for its acceleration in non-convex optimization. To bridge this gap, I will present some non-convex problems where Heavy Ball exhibits provable acceleration guarantees.

In sampling, I will describe how to accelerate a classical sampling method called Hamiltonian Monte Carlo by setting its integration time appropriately, which builds on a connection between sampling and optimization. In machine learning, I will talk about Gradient Descent with pseudo-labels for fast test-time adaptation under the context of tackling distribution shifts.

Event Series Special Seminar Series

Responsible AI: Privacy and Fairness in Decision and Learning Systems

SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United States

Differential Privacy has become the go-to approach for protecting sensitive information in data releases and learning tasks that are used for critical decision processes. For example, census data is used to allocate funds and distribute benefits, while several corporations use machine learning systems for criminal assessments, hiring decisions, and more. While this privacy notion provides strong guarantees, we will show that it may also induce biases and fairness issues in downstream decision processes. These issues may adversely affect many individuals' health, well-being, and sense of belonging, and are currently poorly understood.

In this talk, we delve into the intersection of privacy, fairness, and decision processes, with a focus on understanding and addressing these fairness issues. We first provide an overview of Differential Privacy and its applications in data release and learning tasks. Next, we examine the societal impacts of privacy through a fairness lens and present a framework to illustrate what aspects of the private algorithms and/or data may be responsible for exacerbating unfairness. We hence show how to extend this framework to assess the disparate impacts arising in Machine Learning tasks. Finally, we propose a path to partially mitigate these fairness issues and discuss grand challenges that require further exploration.

Event Series Special Seminar Series

Decoding Nature’s Message Through the Channel of Artificial Intelligence

SDSC, The Synthesis Center 9500 Gilman Drive, La Jolla, CA, United States

Abstract: Nature contains many interesting physics we want to search for, but it cannot speak them out loud. Therefore physicists need to build large particle physics experiments that encode nature's message into experimental data. My research leverages artificial intelligence and machine learning to maximally decode nature's message from those data. The questions I want to ask nature is: Are neutrinos Majorana particles? The answer to this question would fundamentally revise our understanding of physics and the cosmos. Currently, the most effective experimental probe for Majorana neutrino is neutrinoless double-beta decay(0vββ). Cutting-edge AI algorithms could break down significant technological barriers and, in turn, deliver the world's most sensitive search for 0vββ. This talk will discuss one such algorithm, KamNet, which plays a pivotal role in the new result of the KamLAND-Zen experiment. With the help of KamNet, KamLAND-Zen provides a limit that reaches below 50 meV for the first time and is the first search for 0νββ in the inverted mass ordering region. Looking further, the next-generation 0vββ experiment LEGEND has created the Germanium Machine Learning group to aid all aspects of LEGEND analysis and eventually build an independent AI analysis. As the odyssey continues, AI will enlighten the bright future of experimental particle physics.

Event Series Special Seminar Series

Beyond classification: using Machine Learning to probe new physics with the ATLAS experiment in “impossible” final states

SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United States

Abstract: Although the discovery of the Higgs Boson is often referred to as the completion of the Standard Model of Particle Physics, the many outstanding mysteries of our universe indicate that some unknown new physics is awaiting discovery. Machine learning has played an increasingly critical role in searching for this new physics, typically by better separating […]

Event Series Special Seminar Series

Leveraging Simulators for ML Inference in Particle Physics

SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United States

Abstract: The field of research investigating machine-learning (ML) methods that can exploit a physical model of the world through simulators is rapidly growing, particularly for applications in particle physics. While these methods have shown considerable promise in phenomenological studies, they are also known to be susceptible to inaccuracies in the simulators used to train them. In this work, we design a novel analysis strategy that uses the concept of simulation-based inference for a crucial Higgs Boson measurement, where traditional methods are rendered sub-optimal due to quantum interference between Higgs and non-Higgs processes. Our work develops uncertainty quantification methods that account for the impact of inaccuracies in the simulators, uncertainties in the ML predictions themselves, and novel strategies to test the coverage of these quoted uncertainties. These new ML methods leverage the vast computational resources that have recently become available to perform scientific measurements in a way that was not feasible before. In addition, this talk briefly discusses certain ML-bias-mitigation methods developed in particle physics and their potential wider applications.

Earth & Ocean Image Processing Made Easy with MATLAB – Lunch and Learn

Vaughan Hall, Room 100 (UCSD-SIO) 8629 Kennel Way, La Jolla, CA, United States

Join us for a lunch-n-learn technical seminar from our MathWorks team on image processing with MATLAB! MathWorks is looking to create a connection point for conversations about the landscape of computational languages - and the broader impact that software has in academia and industry today. This session explores the basics of pixel-level image processing and high-level machine learning models in MATLAB for images.

Deep Latent Variable Models for Compression and Natural Science | Stephan Mandt

Computer Science & Engineering Building (CSE), Room 1202

Latent variable models have been an integral part of probabilistic machine learning, ranging from simple mixture models to variational autoencoders to powerful diffusion probabilistic models at the center of recent media attention. Perhaps less well-appreciated is the intimate connection between latent variable models and data compression, and the potential of these models for advancing natural science. This talk will explore these topics.

Event Series EnCORE Series: Is There a Theory behind Large ML Models

The Uneasy Relation Between Deep Learning and Statistics

Deep learning uses the language and tools of statistics and classical machine learning, including empirical and population losses and optimizing a hypothesis on a training set. But it uses these tools in regimes where they should not be applicable: the optimization task is non-convex, models are often large enough to overfit, and the training and deployment tasks can radically differ. In this talk I will survey the relation between deep learning and statistics. In particular we will discuss recent works supporting the emerging intuition that deep learning is closer in some aspects to human learning than to classical statistics. Rather than estimating quantities from samples, deep neural nets develop broadly applicable representations and skills through their training. The talk will not assume background knowledge in artificial intelligence or deep learning.

Event Series Special Seminar Series

Uncertainty Quantification for Interpretable Machine Learning | Lili Zheng

Interpretable machine learning has been widely deployed for scientific discoveries and decision-making, while its reliability hinges on the critical role of uncertainty quantification (UQ). In this talk, I will discuss UQ in two challenging scenarios motivated by scientific and societal applications: selective inference for large-scale graph learning and UQ for model-agnostic machine learning interpretations. Specifically, the first part concerns graphical model inference when only irregular, patchwise observations are available, a common setting in neuroscience, healthcare, genomics, and econometrics. To filter out low-confidence edges due to the irregular measurements, I will present a novel inference method that quantifies the uneven edgewise uncertainty levels over the graph as well as an FDR control procedure; this is achieved by carefully disentangling the dependencies across the graph and consequently yields more reliable graph selection. In the second part, I will discuss the computational and statistical challenges associated with UQ for feature importance of any machine learning model. I will take inspiration from recent advances in conformal inference and utilize an ensemble framework to address these challenges. This leads to an almost computationally free, assumption-light, and statistically powerful inference approach for occlusion-based feature importance. For both parts of the talk, I will highlight the potential applications of my research in science and society as well as how it contributes to more reliable and trustworthy data science.

Event Series Distinguished Lecturer Series

The Synergy between Machine Learning and the Natural Sciences | Max Welling

Halıcıoğlu Data Science Institute (HDSI), Room 123 3234 Matthews Ln, La Jolla, CA, United States

Abstract: Traditionally machine learning has been heavily influenced by neuroscience (hence the name artificial neural networks) and physics (e.g. MCMC, Belief Propagation, and Diffusion based Generative AI). We have recently witnessed that the flow of information has also reversed, with new tools developed in the ML community impacting physics, chemistry and biology. Examples include faster DFT, Force-Field accelerated MD simulations, PDE Neural Surrogate models, generating druglike molecules, and many more. In this talk I will review the exciting opportunities for further cross fertilization between these fields, ranging from faster (classical) DFT calculations and enhanced transition path sampling to traveling waves in artificial neural networks.