Month: November 2023

Abstract: This talk will not build on the past sessions of the seminar. We will prove that there is no way to assign risk scores or develop decision-making algorithms that satisfy basic axioms fairness. The talk will begin by giving some examples of (un)fairness in the real world. 2023.12.01 Fairness

Abstract: With the growth of dimensionality of the data we encounter nowadays, the classical gradient descent methods need to speed up. In the presentation, we will start by reviewing the gradient descent and stochastic gradient descent and discuss their theoretical guarantees. After that, more commonly used optimization algorithms in deep learning will be introduced and…