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Found 10 papers, 1 papers with code
The Complexity of Sequential Prediction in Dynamical Systems
We study the problem of learning to predict the next state of a dynamical system when the underlying evolution function is unknown.
Apple Tasting: Combinatorial Dimensions and Minimax Rates
In particular, we show that in the realizable setting, the expected number of mistakes of any learner, under apple tasting feedback, can be $\Theta(1), \Theta(\sqrt{T})$, or $\Theta(T)$.
Online Infinite-Dimensional Regression: Learning Linear Operators
Finally, we prove that the impossibility result and the separation between uniform convergence and learnability also hold in the batch setting.
Multiclass Online Learnability under Bandit Feedback
We study online multiclass classification under bandit feedback.
A Combinatorial Characterization of Supervised Online Learnability
We study the online learnability of hypothesis classes with respect to arbitrary, but bounded loss functions.
Online Learning with Set-Valued Feedback
We study a variant of online multiclass classification where the learner predicts a single label but receives a \textit{set of labels} as feedback.
Multiclass Online Learning and Uniform Convergence
We argue that the best expert has regret at most Littlestone dimension relative to the best concept in the class.
A Characterization of Multioutput Learnability
This provides a complete characterization of the learnability of multilabel classification and multioutput regression in both batch and online settings.
Online Agnostic Multiclass Boosting
In this way, boosting algorithms convert weak learners into strong ones.
Online Boosting for Multilabel Ranking with Top-k Feedback
We present online boosting algorithms for multilabel ranking with top-k feedback, where the learner only receives information about the top k items from the ranking it provides.
