What is surrogate loss in machine learning?

What is surrogate loss in machine learning?

For computational reasons this is usually convex function \Psi: \mathbb{R} \to \mathbb{R}_+. An example of such surrogate loss functions is the hinge loss, \Psi(t) = \max(1-t, 0), which is the loss used by Support Vector Machines (SVMs).

Why surrogate loss function?

In general, the loss function that we care about cannot be optimized efficiently. For example, the 0-1 loss function is discontinuous. So, we consider another loss function that will make our life easier, which we call the surrogate loss function.

How does imitation learning work?

Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions.

What is squared loss?

Squared loss is a loss function that can be used in the learning setting in which we are predicting a real-valued variable y given an input variable x.

What is the loss function for logistic regression?

Log Loss is the loss function for logistic regression. Logistic regression is widely used by many practitioners.

What are some advantages and disadvantages of imitation learning?

advantages: does not need interactive expert, very efficient when trained (in some cases can outperform the demonstrator), has long-term planning. disadvantages: can be difficult to train.

Is behavior cloning supervised learning?

Behavioural cloning is distinct from other forms of imitation learning in that it “treats IL as a supervised learning problem”. That is, it learns to map actions to states the same way a neural network competing in the ImageNet challenge learns to map labels to images.

Why it is called cross-entropy loss?

Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label. So predicting a probability of . A perfect model would have a log loss of 0.