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10 events found.
SDSC, The Auditorium
Events at this venue
The Emergence of General AI for Medicine | Dr. Peter Lee
SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United StatesDr. Peter Lee is Corporate Vice President of Research and Incubations at Microsoft where he leads Microsoft Research and incubates new research-powered products and lines of business in areas such as artificial intelligence, computing foundations, health, and life sciences. He speaks and writes widely on science and technology trends, including the attached NEJM article “Benefits, Limits, […]
On the complexity of Frank-Wolfe methods
SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United StatesAbstract: Frank-Wolfe methods are popular for optimization over a polytope. One of the reasons is because they do not need projection onto the polytope but only linear optimization over it. This talk has two parts. The first part will be about the complexity of Wolfe's method, an algorithm closely related to Frank-Wolfe methods. In 1974 […]
Representation Learning: A Causal Perspective
SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United StatesAbstract: Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data like images and texts. Ideally, such a representation should efficiently capture non-spurious features of the data. It shall also be disentangled so that we can interpret what feature each of its dimensions captures. However, these desiderata are often intuitively defined and challenging to quantify or enforce.
Scaling and Generalizing Approximate Bayesian Inference | David Blei
SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United StatesA core problem in statistics and machine learning is to approximate difficult-to-compute probability distributions. This problem is especially important in Bayesian statistics, which frames all inference about unknown quantities as a calculation about a conditional distribution. In this talk I review and discuss innovations in variational inference (VI), a method that approximates probability distributions through optimization. VI has been used in myriad applications in machine learning and Bayesian statistics.
Proximal MCMC for Bayesian Inference of Constrained and Regularized Estimation
SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United StatesAbstract: In this talk I will introduce some extensions to the proximal Markov Chain Monte Carlo (Proximal MCMC) - a flexible and general Bayesian inference framework for constrained or regularized parametric estimation. The basic idea of Proximal MCMC is to approximate nonsmooth regularization terms via the Moreau-Yosida envelope. Initial proximal MCMC strategies, however, fixed nuisance and regularization parameters as constants, and relied on the Langevin algorithm for the posterior sampling. We extend Proximal MCMC to the full Bayesian framework with modeling and data-adaptive estimation of all parameters including regularization parameters. More efficient sampling algorithms such as the Hamiltonian Monte Carlo are employed to scale Proximal MCMC to high-dimensional problems. Our proposed Proximal MCMC offers a versatile and modularized procedure for the inference of constrained and non-smooth problems that is mostly tuning parameter free. We illustrate its utility on various statistical estimation and machine learning tasks.
Security and Privacy in an Everchanging System Landscape
SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United StatesAbstract: From AI and IoT to AR/VR and Web 3.0, computer systems are evolving at an unprecedented rate. While this evolution has given rise to exciting applications and opportunities, it has also brought about novel security and privacy challenges within these systems and across their interactions with existing platforms. In this talk, I will discuss how system security researchers can keep up with this everchanging landscape and showcase some of my lab's recent work on understanding and detecting malicious web bots. I will explore how we can build and roll out research infrastructure to measure web bot activities and later use our newfound understanding to develop practical solutions to counter them. I will highlight how we can apply similar research principles to areas such as AI and IoT. Finally, I will conclude my talk by previewing some of my ongoing work and outlining my research roadmap toward achieving "security at inception" for emerging systems.
Leveraging Simulators for ML Inference in Particle Physics
SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United StatesAbstract: 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.
Featured
Chatting GPT
SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United StatesArtificial Intelligence (AI) systems have made astonishing progress in the last year. In particular, Large Language Models (LLMs) — AI systems trained on massive amounts of text — have reached a surprising level of capability, with the most recent iterations able to write essays, poems, and computer code, and score near the 90th percentile on […]
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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 StatesAbstract: 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 […]