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Capacity of Fading Channels with Channel Side Information
, 1997
"... We obtain the Shannon capacity of a fading channel with channel side information at the transmitter and receiver, and at the receiver alone. The optimal power adaptation in the former case is "waterpouring" in time, analogous to waterpouring in frequency for timeinvariant frequencysele ..."
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Cited by 579 (23 self)
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We obtain the Shannon capacity of a fading channel with channel side information at the transmitter and receiver, and at the receiver alone. The optimal power adaptation in the former case is "waterpouring" in time, analogous to waterpouring in frequency for timeinvariant frequency
Distance Metric Learning, With Application To Clustering With SideInformation
 ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 15
, 2003
"... Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as Kmeans initially fails to find one that is meaningful to a user, the only recourse may be for the us ..."
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Cited by 799 (14 self)
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Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as Kmeans initially fails to find one that is meaningful to a user, the only recourse may be for the user to manually tweak the metric until sufficiently good clusters are found. For these and other applications requiring good metrics, it is desirable that we provide a more systematic way for users to indicate what they consider "similar." For instance, we may ask them to provide examples. In this paper, we present an algorithm that, given examples of similar (and, if desired, dissimilar) pairs of points in R , learns a distance metric over R that respects these relationships. Our method is based on posing metric learning as a convex optimization problem, which allows us to give efficient, localoptimafree algorithms. We also demonstrate empirically that the learned metrics can be used to significantly improve clustering performance.
The ratedistortion function for source coding with side information at the decoder
 IEEE Trans. Inform. Theory
, 1976
"... AbstractLet {(X,, Y,J}r = 1 be a sequence of independent drawings of a pair of dependent random variables X, Y. Let us say that X takes values in the finite set 6. It is desired to encode the sequence {X,} in blocks of length n into a binary stream*of rate R, which can in turn be decoded as a seque ..."
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Cited by 1055 (1 self)
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sequence { 2k}, where zk E %, the reproduction alphabet. The average distorjion level is (l/n) cl = 1 E[D(X,,z&, where D(x, $ 2 0, x E I, 2 E J, is a preassigned distortion measure. The special assumption made here is that the decoder has access to the side information {Yk}. In this paper we determine
On the capacity of MIMO broadcast channel with partial side information
 IEEE TRANS. INFORM. THEORY
, 2005
"... In multipleantenna broadcast channels, unlike pointtopoint multipleantenna channels, the multiuser capacity depends heavily on whether the transmitter knows the channel coefficients to each user. For instance, in a Gaussian broadcast channel with transmit antennas and singleantenna users, the ..."
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Cited by 344 (9 self)
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, the sum rate capacity scales like log log for large if perfect channel state information (CSI) is available at the transmitter, yet only logarithmically with if it is not. In systems with large, obtaining full CSI from all users may not be feasible. Since lack of CSI does not lead to multiuser gains
Universal Portfolios with Side Information
 IEEE Transactions on Information Theory
, 1996
"... We present a sequential investment algorithm, the ¯weighted universal portfolio with sideinformation, which achieves, to first order in the exponent, the same wealth as the best sideinformation dependent investment strategy (the best stateconstant rebalanced portfolio) determined in hindsight fr ..."
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Cited by 117 (4 self)
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We present a sequential investment algorithm, the ¯weighted universal portfolio with sideinformation, which achieves, to first order in the exponent, the same wealth as the best sideinformation dependent investment strategy (the best stateconstant rebalanced portfolio) determined in hindsight
Index Coding with Side Information
, 2006
"... Motivated by a problem of transmitting supplemental data over broadcast channels (Birk and Kol, INFOCOM 1998), we study the following coding problem: a sender communicates with n receivers R1,..., Rn. He holds an input x ∈ {0, 1} n and wishes to broadcast a single message so that each receiver Ri c ..."
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Cited by 105 (0 self)
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can recover the bit xi. Each Ri has prior side information about x, induced by a directed graph G on n nodes; Ri knows the bits of x in the positions {j  (i, j) is an edge of G}. G is known to the sender and to the receivers. We call encoding schemes that achieve this goal INDEX codes for {0, 1} n
with Side Information
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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Cited by 1 (0 self)
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
S.: Boosting with side information
 In: ACCV
, 2013
"... Abstract. In many problems of machine learning and computer vision, there exists side information, i.e., information contained in the training data and not available in the testing phase. This motivates the recent development of a new learning approach known as learning with side information that ..."
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Cited by 4 (1 self)
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Abstract. In many problems of machine learning and computer vision, there exists side information, i.e., information contained in the training data and not available in the testing phase. This motivates the recent development of a new learning approach known as learning with side information
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