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Cognitive Radio: BrainEmpowered Wireless Communications
, 2005
"... Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a softwaredefined radio, is defined as an intelligent wireless communication system that is aware of its environment and use ..."
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Cited by 1479 (4 self)
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Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a softwaredefined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understandingbybuilding to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: • highly reliable communication whenever and wherever needed; • efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radioscene analysis. 2) Channelstate estimation and predictive modeling. 3) Transmitpower control and dynamic spectrum management. This paper also discusses the emergent behavior of cognitive radio.
to NoRegret Online Learning
"... Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. This leads to poor performance in theory and often in practice. Some recent approaches (Daumé III et al., ..."
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Cited by 1 (1 self)
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Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. This leads to poor performance in theory and often in practice. Some recent approaches (Daumé III et al., 2009; Ross and Bagnell, 2010) provide stronger guarantees in this setting, but remain somewhat unsatisfactory as they train either nonstationary or stochastic policies and require a large number of iterations. In this paper, we propose a new iterative algorithm, which trains a stationary deterministic policy, that can be seen as a no regret algorithm in an online learning setting.
Noregret learning in convex games
, 2007
"... Quite a bit is known about minimizing different kinds of regret in experts problems, and how these regret types relate to types of equilibria in the multiagent setting of repeated matrix games. Much less is known about the possible kinds of regret in online convex programming problems (OCPs), or abo ..."
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Cited by 19 (4 self)
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Quite a bit is known about minimizing different kinds of regret in experts problems, and how these regret types relate to types of equilibria in the multiagent setting of repeated matrix games. Much less is known about the possible kinds of regret in online convex programming problems (OCPs), or about equilibria in the analogous multiagent setting of repeated convex games. This gap is unfortunate, since convex games are much more expressive than matrix games, and since many important machine learning problems can be expressed as OCPs. In this paper, we work to close this gap: we analyze a spectrum of regret types which lie between external and swap regret, along with their corresponding equilibria, which lie between coarse correlated and correlated equilibrium. We also analyze algorithms for minimizing these regret types. As examples of our framework, we derive algorithms for learning correlated equilibria in polyhedral convex games and extensiveform correlated equilibria in extensiveform games. The former is exponentially more efficient than previous algorithms, and the latter is the first of its type. 1.
Efficient noregret multiagent learning
 Proceedings of The 20th National Conference on Artificial Intelligence
, 2005
"... We present new results on the efficiency of noregret algorithms in the context of multiagent learning. We use a known approach to augment a large class of noregret algorithms to allow stochastic sampling of actions and observation of scalar reward of only the action played. We show that the avera ..."
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Cited by 13 (0 self)
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We present new results on the efficiency of noregret algorithms in the context of multiagent learning. We use a known approach to augment a large class of noregret algorithms to allow stochastic sampling of actions and observation of scalar reward of only the action played. We show
NoRegret Reductions for Imitation Learning and Structured Prediction
 In AISTATS
, 2011
"... Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. This leads to poor performance in theory and often in practice. Some recent approaches (Daumé III et al., ..."
Abstract

Cited by 65 (13 self)
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Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. This leads to poor performance in theory and often in practice. Some recent approaches (Daumé III et al., 2009; Ross and Bagnell, 2010) provide stronger guarantees in this setting, but remain somewhat unsatisfactory as they train either nonstationary or stochastic policies and require a large number of iterations. In this paper, we propose a new iterative algorithm, which trains a stationary deterministic policy, that can be seen as a no regret algorithm in an online learning setting. We show that any such no regret algorithm, combined with additional reduction assumptions, must find a policy with good performance under the distribution of observations it induces in such sequential settings. We demonstrate that this new approach outperforms previous approaches on two challenging imitation learning problems and a benchmark sequence labeling problem. 1
Noregret with bounded computational capacity
 CMSEMS Discussion Paper 1373, Kellogg School of Management, Northwestern
, 2003
"... We deal with no regret and related aspects of vectorpayoff games when one of the players is limited in computational capacity. We show that player 1 can almost approach with boundedrecall strategies, or with finite automata, any convex set which is approachable when no capacity bound is present. I ..."
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Cited by 1 (1 self)
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We deal with no regret and related aspects of vectorpayoff games when one of the players is limited in computational capacity. We show that player 1 can almost approach with boundedrecall strategies, or with finite automata, any convex set which is approachable when no capacity bound is present. In particular we deduce that with bounded computational capacity player 1 can ensure having almost no regret.
A wide range noregret theorem
 Games and Economic Behavior
"... Abstract. In a sequential decision problem at any stage a decision maker, based on the history, takes a decision and receives a payoff which depends also on the realized state of nature. A strategy, f, is said to be as good as an alternative strategy g at a sequence of states, if in the long run f d ..."
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Cited by 29 (2 self)
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Abstract. In a sequential decision problem at any stage a decision maker, based on the history, takes a decision and receives a payoff which depends also on the realized state of nature. A strategy, f, is said to be as good as an alternative strategy g at a sequence of states, if in the long run f does, on average, at least as well as g does. It is shown that for any distribution, µ, over the alternative strategies there is a strategy f which is, at any sequence of states, as good as µalmost any alternative g. 1I am grateful to Ehud Kalai, Rann Smorodinsky and Eilon Solan for fruitful discussions on this subject. I am also grateful to two anonymous referees of Games and Economic Behavior. This research was partially supported by the Israel Science Foundation, Grant no. 178/9910.0. 1.
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