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HDSI Seminar Series: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate by Quanquan Gu

Title: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate HDSI Seminar Series Quanquan Gu, Assistant Professor of Computer Science at UCLA Abstract: Understanding the algorithmic bias of stochastic gradient descent (SGD) is one of the key challenges in modern machine learning and deep learning theory. Most of the existing works, however, focus […]

HDSI Seminar Series: Deep Learning for Market Design: Fairness, Robustness, and Expressiveness by John Dickerson

Title Deep Learning for Market Design: Fairness, Robustness, and Expressiveness John Dickerson, Assistant Professor of Computer Science, University of Maryland; Chief Scientist, Arthur AI AbstractThe design of revenue-maximizing auctions with strong incentive guarantees is a core concern of economic theory. Computational auctions enable online advertising, sourcing, spectrum allocation, and myriad financial markets. Analytic progress in this […]

Event Series Colloquia Lecture Series

Structured Transformer Models for NLP

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

The field of natural language processing has recently unlocked a wide range of new capabilities through the use of large language models, such as GPT-4. The growing application of these models motivates developing a more thorough understanding of how and why they work, as well as further improvements in both quality and efficiency.

In this talk, I will present my work on analyzing and improving the Transformer architecture underlying today's language models through the study of how information is routed between multiple words in an input. I will show that such models can predict the syntactic structure of text in a variety of languages, and discuss how syntax can inform our understanding of how the networks operate. I will also present my work on structuring information flow to build radically more efficient models, including models that can process text of up to one million words, which enables new possibilities for NLP with book-length text.

Scaling and Generalizing Approximate Bayesian Inference | David Blei

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

A 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.

Event Series Colloquia Lecture Series

Algorithms for multi-group learning

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

Abstract: Multi-group agnostic learning is a formal learning criterion that is concerned with the conditional risks of predictors within subgroups of a population. The criterion addresses recent practical concerns such as subgroup fairness and hidden stratification. I'll talk about the structure of solutions to the multi-group learning problem, as well as some simple and near-optimal algorithms for the learning problem. This is based on joint work with Christopher Tosh.

Event Series Colloquia Lecture Series

Spectral clustering in high-dimensional Gaussian mixture block models

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

The Gaussian mixture block model is a simple generative model for networks: to generate a sample, we associate each node with a latent feature vector sampled from a mixture of Gaussians, and we add an edge between nodes if and only if their feature vectors are sufficiently similar. The different components of the Gaussian mixture […]

Event Series Colloquia Lecture Series

Representation Learning: A Causal Perspective

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

Abstract: 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.

Event Series Colloquia Lecture Series

On the complexity of Frank-Wolfe methods

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

Abstract: 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 […]

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

CSE 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.