554 research outputs found
Online Learning of Quantum States
Suppose we have many copies of an unknown -qubit state . We measure
some copies of using a known two-outcome measurement , then other
copies using a measurement , and so on. At each stage , we generate a
current hypothesis about the state , using the outcomes of
the previous measurements. We show that it is possible to do this in a way that
guarantees that , the error in our prediction for the next
measurement, is at least at most times. Even in the "non-realizable" setting---where
there could be arbitrary noise in the measurement outcomes---we show how to
output hypothesis states that do significantly worse than the best possible
states at most times on the first
measurements. These results generalize a 2007 theorem by Aaronson on the
PAC-learnability of quantum states, to the online and regret-minimization
settings. We give three different ways to prove our results---using convex
optimization, quantum postselection, and sequential fat-shattering
dimension---which have different advantages in terms of parameters and
portability.Comment: 18 page
Efficiently Learning from Revealed Preference
In this paper, we consider the revealed preferences problem from a learning
perspective. Every day, a price vector and a budget is drawn from an unknown
distribution, and a rational agent buys his most preferred bundle according to
some unknown utility function, subject to the given prices and budget
constraint. We wish not only to find a utility function which rationalizes a
finite set of observations, but to produce a hypothesis valuation function
which accurately predicts the behavior of the agent in the future. We give
efficient algorithms with polynomial sample-complexity for agents with linear
valuation functions, as well as for agents with linearly separable, concave
valuation functions with bounded second derivative.Comment: Extended abstract appears in WINE 201
Efficient Classification for Metric Data
Recent advances in large-margin classification of data residing in general
metric spaces (rather than Hilbert spaces) enable classification under various
natural metrics, such as string edit and earthmover distance. A general
framework developed for this purpose by von Luxburg and Bousquet [JMLR, 2004]
left open the questions of computational efficiency and of providing direct
bounds on generalization error.
We design a new algorithm for classification in general metric spaces, whose
runtime and accuracy depend on the doubling dimension of the data points, and
can thus achieve superior classification performance in many common scenarios.
The algorithmic core of our approach is an approximate (rather than exact)
solution to the classical problems of Lipschitz extension and of Nearest
Neighbor Search. The algorithm's generalization performance is guaranteed via
the fat-shattering dimension of Lipschitz classifiers, and we present
experimental evidence of its superiority to some common kernel methods. As a
by-product, we offer a new perspective on the nearest neighbor classifier,
which yields significantly sharper risk asymptotics than the classic analysis
of Cover and Hart [IEEE Trans. Info. Theory, 1967].Comment: This is the full version of an extended abstract that appeared in
Proceedings of the 23rd COLT, 201
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