Author Archives: czxttkl

I have always had some doubts on grid search. I am not sure how I should conduct grid search for hyperparameter tuning for a model and report the model’s generalization performance for a scientific paper. There are three possible ways: 1)  Split data into 10 folds. Repeat 10 times of the following: pick 9 folds as training data, …

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Monte Carlo Tree Search (MCTS) has been successfully applied in complex games such as Go [1]. In this post, I am going to introduce some basic concepts of MCTS and its application. MCTS is a method for finding optimal decisions in a given domain by taking random samples in the decision space and building a …

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Cross Entropy (CE) method is a general Monte Carlo method originally proposed to estimate rare-event probabilities but then naturally extended to solve optimization problems. It is relevant to several my previous posts. For example, both Bayesian Optimization [5] and CE method can be used to solve black-box optimization problems, although Bayesian Optimization mostly works on continuous input …

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In this post, I am going to share my understanding in Bayesian Optimization. Bayesian Optimization is closely related Gaussian Process, a probabilistic model explained in details in a textbook called “Gaussian Processes for Machine Learning” [1]. In the following, “p. ” will refer to a specific page in [1] where the content comes from.  First, let’s dive into the …

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In this post, I am going to walk through an online piece of code [7] which implements the idea of [1]: using pointer network [2] to solve travelling salesman problem. Pointer networks, in my understanding, are neural network architectures for the problems where output sequences come from the permutation of input sequences. Some background posts …

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This post reviews the key knowledgement about support vector machine (SVM). The post is pretty much based on a series of the lectures [1]. Suppose we have data , where each point is a -dimensional data point associated with label . The very initial SVM is to come up with a plane such that data …

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Travelling salesman problem (TSP) goes as follows [1]: Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city and returns to the origin city? This problem statement is actually a TSP-OPT problem., where”OPT” stands for optimization. This is not a decision problem …

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