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Online learning: Stochastic, constrained, and smoothed adversaries
Learning theory has largely focused on two main learning scenarios: the classical statistical
setting where instances are drawn iid from a fixed distribution, and the adversarial scenario …
setting where instances are drawn iid from a fixed distribution, and the adversarial scenario …
Efficient algorithms for learning functions with bounded variation
PM Long - Information and Computation, 2004 - Elsevier
We show that the class F BV of [0, 1]-valued functions with total variation at most 1 can be
agnostically learned with respect to the absolute loss in polynomial time from O 1 ϵ 2log 1 δ …
agnostically learned with respect to the absolute loss in polynomial time from O 1 ϵ 2log 1 δ …
[PDF][PDF] On the sample complexity of learning functions with bounded variation
PM Long - Proceedings of the eleventh annual conference on …, 1998 - dl.acm.org
We show that the class 3nv of [0, l]-valued functions with total variation at most 1 can be
agnostically learned with respect to the absolute loss in polynomial time from 0 (5 log+) …
agnostically learned with respect to the absolute loss in polynomial time from 0 (5 log+) …
[کتاب][B] Nonparametric estimation, regression, and prediction under minimal regularity conditions
SE Posner - 1995 - search.proquest.com
We explore consistent nonparametric techniques in the contexts of estimation, regression,
and prediction. The common element of the topics is that each one can be viewed as a noisy …
and prediction. The common element of the topics is that each one can be viewed as a noisy …
[فهرست منابع][C] On bandit problems with side observations and learnability
SR Kulkarni - Proc. 31st Allerton Conf. Commun. Contr. Comp, 1993