Questions tagged [sample-complexity]

For questions related to the sample complexity of a machine learning algorithm, which represents the number of training-samples that it needs in order to successfully learn a target function. More precisely, the sample complexity is the number of training-samples that we need to supply to the algorithm, so that the function returned by the algorithm is within an arbitrarily small error of the best possible function, with probability arbitrarily close to 1.

Filter by
Sorted by
Tagged with
1
vote
0answers
14 views

What is the sample complexity of Monte Carlo Exploring Starts in RL?

We can use a model-free Monte Carlo approach to solving an MDP $(S,A,R,P,\gamma)$ with transition dynamics $P$ unknown by estimating Q-values by rolling out trajectories starting from random states $...
1
vote
0answers
48 views

Is it better to split sequences into overlapping or non-overlapping training samples?

I have $N$ (time) sequences of data with length $2048$. Each of these sequences correseponds to a different target output. However, I know that only a small part of the sequence is needed to actually ...
2
votes
0answers
42 views

Can you find another reason for sample inefficiency of model-free on-policy Deep Reinforcement Learning?

The following mindmap gives an overview of multiple reasons for sample inefficiency. The list is definitely not complete. Can you see another reason not mentioned so far? Some related links: ...
2
votes
1answer
71 views

Is there any practical application of knowing whether a concept class is PAC-learnable?

A concept class $C$ is PAC-learnable if there exists an algorithm that can output a hypothesis with probability at least $(1-\delta)$ (the "probably" part), and an error that is less than $\epsilon$ (...
5
votes
4answers
1k views

How does size of the dataset depend on VC dimension of the hypothesis class?

This might be a little broad question, but I have been watching Caltech youtube videos on Machine Learning, and in this video prof. is trying to explain how we should interpret the VC dimension in ...