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MadaBoost: A Modification of AdaBoost
TLDR
A new boosting algorithm MadaBoost is proposed that can be casted in the statistical query learning model [Kea93] and thus, it is robust to random classification noise [AL88]. Expand
Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms
TLDR
This paper proposes an adaptive sampling algorithm that solves a general problem covering many problems arising in applications of discovery science, and describes how different instantiations of it can be applied to scale up knowledge discovery problems that appear in several areas. Expand
Scaling Up a Boosting-Based Learner via Adaptive Sampling
TLDR
This paper presents an experimental evaluation of a boosting based learning system that can be run efficiently over a large dataset and provides experimental evidence that the method is as accurate as the equivalent algorithm that uses all the dataset but much faster. Expand
Efficient Read-Restricted Monotone CNF/DNF Dualization by Learning with Membership Queries
TLDR
An incremental output polynomial time algorithm is given for exact learning both the read-k CNF and (not necessarily read restricted) DNF descriptions of f, the only method of obtaining information about f through membership queries. Expand
Non-Automatizability of Bounded-Depth Frege Proofs
Abstract.In this paper, we show how to extend the argument due to Bonet, Pitassi and Raz to show that bounded-depth Frege proofs do not have feasible interpolation, assuming that factoring of BlumExpand
Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms
TLDR
This paper proposes an adaptive sampling method that solves a general problem covering many actual problems arising in applications of discovery science, and proves the correctness of the method and estimates its efficiency theoretically. Expand
Non-automatizability of bounded-depth Frege proofs
In this paper; we show how to extend the argument due to Bonet, Pitassi and Raz to show that bounded-depth Frege proofs do not have feasible interpolation, assuming that factoring of Blum integers orExpand
Polynominal Time Algorithms for Some Self-Duality Problems
TLDR
This paper exhibits polynomial time algorithms for testing self-duality for several natural classes of formulas where the problem was not known to be solvable. Expand
Faster Near-Optimal Reinforcement Learning: Adding Adaptiveness to the E3 Algorithm
TLDR
This paper shows how the algorithm can be improved by substituting the exploration phase, that builds a model of the underlying Markov decision process by estimating the transition probabilities, by an adaptive sampling method more suitable for the problem. Expand
Practical Algorithms for On-line Sampling
TLDR
This paper presents two on-line sampling algorithms for selecting a hypothesis, gives theoretical bounds on the number of examples needed, and analyses them experimentally to study the problem of how to determine which of the hypotheses in the class is almost the best one. Expand
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