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Events
Upcoming Events
CS Colloquium - "Learning Sparse Dependence Structure in Complex-Valued Tensor Data"
CS Colloquium - "Approximation Algorithms: Some ancient, some new - the good, the bad and the ugly"
Past Events
Dynamical Systems and Neuronal Dynamics
Speaker: Yangyang Wang, Department of Mathematics
Optimization & approximation: the mathematical challenges of machine learning
Speaker: David Stewart, Department of Mathematics
Evidence Synthesis
Speaker: Ariel Aloe, Department of Psychological and Quantitative Foundations
Introduction to Scientific Machine Learning
Speaker: Xueyu Zhu, Department of Mathematics.
Intelligent traffic light via policy-based reinforcement learning
Speaker: Shaoping Xiao, Department of Mechanical Engineering
Flattening the error curve of predictors for implicit methods in IVPs
Speaker: Laurent Jay, Department of Mathematics
GAUSS Seminar: Numbers and Games [hybrid]
This talk will focus itself on games. Some basic games will be introduced and their strategies analyzed. We will scratch the surface of combinatorial game theory, a lovely, playful, and often overlooked branch of mathematics. In the process we will stumble upon the surreal numbers and explore the very nature of “numbers”. This talk will be accessible for all audiences. There is no prerequisite knowledge needed, just an open mind.
We will have milk and cookies! Remember to bring your...
Colloquium - Programming Languages Techniques for Controlling Generalization Errors in Adaptive Data Analysis
Marco Gaboardi (Boston University)
AbstractData analysts aim at guaranteeing that the result of a data analysis run on sample data does not differ too much from the result one would achieve by running the analysis over the entire population. To achieve this goal, they have developed several techniques to control the generalization errors of their data analyses. In this talk, I will discuss how programming language techniques can help data analysts to design adaptive data analyses...
Colloquium - On Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features
Yingyu Liang
AbstractAn important characteristic of neural networks is their ability to learn representations of the input data with effective features for prediction, which is believed to be a key factor to their superior empirical performance. To better understand the source and benefit of feature learning in neural networks, we consider learning problems motivated by practical data, where the labels are determined by a set of class relevant patterns and the inputs are generated...
GAUSS Seminar: Puzzles, Ice, & Grothendieck Polynomials [hybrid]
We introduce quivers, path algebras and their representations. Then, in the case when our ground field is algebraically closed, we discuss a particular Morita invariant of path algebras arising from finite quivers, the Ext quiver of the category. Through examples we see how to compute the Ext quiver using quiver representations and techniques from linear algebra. We aim to keep the talk accessible to undergraduate and graduate students alike.
SpeakerRyan Bianconi UI Mathematics PhD...