Under this setting, and for the right choice of loss function and "mixing" algorithm, it is possible for the learner to achieve a constant regret regardless of the number of prediction rounds.

Under this setting, and for the right choice of loss function and "mixing" algorithm, it is possible for the learner to achieve a constant regret regardless of the number of prediction rounds.

We formalize and study the natural approach of designing convex surrogate loss functions via embeddings for problems such as classification or ranking.

If the labels are given from a discrete set, the trees are usually called classification trees, and otherwise they are usually called regression trees .

WW hinge loss and so on.

Distributionally robust optimization (DRO) is a widely-used approach to learn models that are robust against distribution shift.

It seems you misunderstood some key points and details. Hope our explanation below could help to clarify some misunderstandings and confusion. By "specific learning rate schedule", we think We think the empirical evidence is sufficient to verify our theoretical claims. This is exactly the case here. Figure 1(b) in [Triantafillou et al. 2020] shows that the increase of shots For your other comments: 1) The inner-task gap vanishes because the expectation of the loss function w.r.t.

We formalize and study the natural approach of designing convex surrogate loss functions via embeddings for problems such as classification or ranking.

It seems you misunderstood some key points and details. Hope our explanation below could help to clarify some misunderstandings and confusion. By "specific learning rate schedule", we think We think the empirical evidence is sufficient to verify our theoretical claims. This is exactly the case here. Figure 1(b) in [Triantafillou et al. 2020] shows that the increase of shots For your other comments: 1) The inner-task gap vanishes because the expectation of the loss function w.r.t.