## Difficulty of Predicting the Maximum of Gaussians

Suppose that we have a random variable $X \in \mathbb{R}^d$, such that $\mathbb{E}[XX^{\top}] = I_{d \times d}$. Now take k independent Gaussian random variables $Z_1, \ldots, Z_

Suppose that we have a random variable $X \in \mathbb{R}^d$, such that $\mathbb{E}[XX^{\top}] = I_{d \times d}$. Now take k independent Gaussian random variables $Z_1, \ldots, Z_

For collections of independent random variables, the Chernoff bound and related bounds give us very sharp concentration inequalities --- if $X_1,\ldots,X_n$ are independent, then their sum has a distribution

(This is available in pdf form here.) If you are a newly initiated student into the field of machine learning, it won't be long before you start hearing the words "Bayesian"

Humans are very good at correctly generalizing rules across categories (at least, compared to computers). In this post I will examine mechanisms that would allow us to do this in a reasonably rigorous

What happens when you are uncertain about observations you made? For instance, you remember something happening, but you don't remember who did it. Or you remember some fact you read on wikipedia, but