Conformal prediction is a method to sequentially predict consistent confidence intervals using nonconformity measures.

The [[Ratio Cut]] Graph Cuts Cut For a subset of nodes $\mathcal A\subset \mathcal V$, the rest of …

It was discovered that the success of [[mutual information based contrastive learning]] Contrastive …

Normalizing flow is a method to convert a complicated distribution $p(x)$ to a simpler distribution …

In GAN, the latent space input is usually random noise, e.g., Gaussian noise. The objective of …

logistics regression is a simple model for classification

Two of the key elements in a learning problem are: a set of hypothesis $\mathcal H$, and a set of …

random forest in machine learning

Detecting correlations using Pearson's chi square correlation test

Essential knowledge of internet

unsupervised learning: support vector machine

Python as a programming language

mind the data structure: here comes the tree

C++ as a programming language

Boltzmann machine is much like a spin glass model in physics. In short words, Boltzmann machine is a machine that has nodes that can take values, and the nodes are connected through some weight. It is just like any other neual nets but with complications and theoretical implications.

The Neyman-Pearson hypothesis testing tests two hypothesis, hypothesis $H$, and an alternative …

Graphs can be used in many problem and there are many possible problems on graphs. We will mention a …

Contrastive Predictive Coding, aka CPC, is an autoregressive model combined with InfoNCE loss1. …

Max Global Mutual Information Why not just use the global mutual information of the input and …

Autoencoders (AE) are machines that encodes inputs into a compact latent space. The simplest …