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CORR
2011
Springer
178views Education» more  CORR 2011»
12 years 11 months ago
Online Learning: Stochastic and Constrained Adversaries
Learning theory has largely focused on two main learning scenarios. The first is the classical statistical setting where instances are drawn i.i.d. from a fixed distribution and...
Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
COCO
2006
Springer
118views Algorithms» more  COCO 2006»
13 years 11 months ago
Learning Monotone Decision Trees in Polynomial Time
We give an algorithm that learns any monotone Boolean function f : {-1, 1}n {-1, 1} to any constant accuracy, under the uniform distribution, in time polynomial in n and in the de...
Ryan O'Donnell, Rocco A. Servedio
DMIN
2010
262views Data Mining» more  DMIN 2010»
13 years 5 months ago
SMO-Style Algorithms for Learning Using Privileged Information
Recently Vapnik et al. [11, 12, 13] introduced a new learning model, called Learning Using Privileged Information (LUPI). In this model, along with standard training data, the tea...
Dmitry Pechyony, Rauf Izmailov, Akshay Vashist, Vl...
ALT
2011
Springer
12 years 7 months ago
Semantic Communication for Simple Goals Is Equivalent to On-line Learning
Abstract. Previous works [11, 6] introduced a model of semantic communication between a “user” and a “server,” in which the user attempts to achieve a given goal for commun...
Brendan Juba, Santosh Vempala
ICML
2007
IEEE
14 years 8 months ago
Multi-armed bandit problems with dependent arms
We provide a framework to exploit dependencies among arms in multi-armed bandit problems, when the dependencies are in the form of a generative model on clusters of arms. We find ...
Sandeep Pandey, Deepayan Chakrabarti, Deepak Agarw...