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Correlated Qlearning
 In Proceedings of the Twentieth International Conference on Machine Learning
, 2003
"... There have been several attempts to design multiagent Qlearning algorithms capable of learning equilibrium policies in generalsum Markov games, just as Qlearning learns optimal policies in Markov decision processes. We introduce correlated Qlearning, one such algorithm based on the correlated eq ..."
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Cited by 62 (2 self)
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There have been several attempts to design multiagent Qlearning algorithms capable of learning equilibrium policies in generalsum Markov games, just as Qlearning learns optimal policies in Markov decision processes. We introduce correlated Qlearning, one such algorithm based on the correlated
CorrelatedQ learning
 In NIPS Workshop on Multiagent Learning
, 2002
"... Bowling named two desiderata for multiagent learning algorithms: rationality and convergence. This paper introduces co~elatedQ learning, a natural generalization of NashQ and FFQ that satisfies these criteria. NashoQ satisfies rationality, but in general it does not converge. FFQ satisfies conve ..."
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Cited by 64 (2 self)
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Bowling named two desiderata for multiagent learning algorithms: rationality and convergence. This paper introduces co~elatedQ learning, a natural generalization of NashQ and FFQ that satisfies these criteria. NashoQ satisfies rationality, but in general it does not converge. FFQ satisfies
Bayesian Qlearning
 In AAAI/IAAI
, 1998
"... A central problem in learning in complex environments is balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be estimated using the classical notion of Value of Information the expected improvement in future decision ..."
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Cited by 144 (1 self)
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Watkins' Qlearning by maintaining and propagating probability distributions over the Qvalues. These distributions are used to compute a myopic approximation to the value of information for each action and hence to select the action that best balances exploration and exploitation. We establish
Investor psychology and security market under and overreactions
 Journal of Finance
, 1998
"... We propose a theory of securities market under and overreactions based on two wellknown psychological biases: investor overconfidence about the precision of private information; and biased selfattribution, which causes asymmetric shifts in investors ’ confidence as a function of their investment ..."
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Cited by 661 (38 self)
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outcomes. We show that overconfidence implies negative longlag autocorrelations, excess volatility, and, when managerial actions are correlated with stock mispricing, publiceventbased return predictability. Biased selfattribution adds positive shortlag autocorrelations ~“momentum”!, short
Teleporting an Unknown Quantum State via Dual Classical and EPR Channels
, 1993
"... An unknown quantum state jOEi can be disassembled into, then later reconstructed from, purely classical information and purely nonclassical EPR correlations. To do so the sender, "Alice," and the receiver, "Bob," must prearrange the sharing of an EPRcorrelated pair of particles. ..."
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Cited by 648 (22 self)
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An unknown quantum state jOEi can be disassembled into, then later reconstructed from, purely classical information and purely nonclassical EPR correlations. To do so the sender, "Alice," and the receiver, "Bob," must prearrange the sharing of an EPRcorrelated pair of particles
Improved prediction of signal peptides  SignalP 3.0
 J. MOL. BIOL.
, 2004
"... We describe improvements of the currently most popular method for prediction of classically secreted proteins, SignalP. SignalP consists of two different predictors based on neural network and hidden Markov model algorithms, where both components have been updated. Motivated by the idea that the cle ..."
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Cited by 655 (7 self)
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that the cleavage site position and the amino acid composition of the signal peptide are correlated, new features have been included as input to the neural network. This addition, combined with a thorough errorcorrection of a new data set, have improved the performance of the predictor significantly over Signal
Local features and kernels for classification of texture and object categories: a comprehensive study
 International Journal of Computer Vision
, 2007
"... Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a largescale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations an ..."
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Cited by 644 (35 self)
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and learns a Support Vector Machine classifier with kernels based on two effective measures for comparing distributions, the Earth Mover’s Distance and the χ 2 distance. We first evaluate the performance of our approach with different keypoint detectors and descriptors, as well as different kernels
The Cyclical Behavior of Equilibrium Unemployment and Vacancies
 American Economic Review
, 2005
"... This paper argues that a broad class of search models cannot generate the observed businesscyclefrequency fluctuations in unemployment and job vacancies in response to shocks of a plausible magnitude. In the U.S., the vacancyunemployment ratio is 20 times as volatile as average labor productivity ..."
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Cited by 839 (20 self)
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of the model. I show that a shock that changes average labor productivity primarily alters the present value of wages, generating only a small movement along a downward sloping Beveridge curve (unemploymentvacancy locus). A shock to the job destruction rate generates a counterfactually positive correlation
The Lifting Scheme: A Construction Of Second Generation Wavelets
, 1997
"... . We present the lifting scheme, a simple construction of second generation wavelets, wavelets that are not necessarily translates and dilates of one fixed function. Such wavelets can be adapted to intervals, domains, surfaces, weights, and irregular samples. We show how the lifting scheme leads to ..."
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Cited by 541 (16 self)
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. This is based on the fact that most data sets have correlation both in time (or space) and frequenc...
Results 1  10
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