From on-line to batch learning
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
Toward Efficient Agnostic Learning
Machine Learning - Special issue on computational learning theory, COLT'92
Using and combining predictors that specialize
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Proof verification and the hardness of approximation problems
Journal of the ACM (JACM)
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Some optimal inapproximability results
Journal of the ACM (JACM)
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Agnostically Learning Halfspaces
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Prediction, Learning, and Games
Prediction, Learning, and Games
From External to Internal Regret
The Journal of Machine Learning Research
On the generalization ability of on-line learning algorithms
IEEE Transactions on Information Theory
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We study the online decision problem where the set of available actions varies over time, also called the sleeping experts problem. We consider the setting where the performance comparison is made with respect to the best ordering of actions in hindsight. In this paper, both the payoff function and the availability of actions is adversarial. Kleinberg et al. (2008) gave a computationally efficient no-regret algorithm in the setting where payoffs are stochastic. Kanade et al. (2009) gave an efficient no-regret algorithm in the setting where action availability is stochastic. However, the question of whether there exists a computationally efficient no-regret algorithm in the adversarial setting was posed as an open problem by Kleinberg et al. (2008). We show that such an algorithm would imply an algorithm for PAC learning DNF, a long standing important open problem. We also show that a related problem, the gambling problem, posed as an open problem by Abernethy (2010) is related to agnostically learning halfspaces, albeit under restricted distributions.