Events
Upcoming Events
Rising Stars! - CS Colloquium
Interplay between topics from math with such neighboring areas as physics, statistics, finance, and engineering.
Speaker: Palle Jorgensen, Dept. of Mathematics
Past Events
Logarithmic Lipschitz Norms and Diffusion-Induced instability
Speaker: Zahra Aminzare, Dept. of Mathematics
Decision Making under Uncertainty: An Optimization Perspective
Speaker: Beste Basciftci, Dept. of Business Analytics
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] promotional image](/sites/amcs.uiowa.edu/files/styles/square__1024_x_1024/public/externals/8/8/882bff4192c98b6daecf3aec284b6a97.png?itok=M9_gUgOc)
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...