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Electronic Colloquium on Computational Complexity

Under the auspices of the Computational Complexity Foundation (CCF)

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Reports tagged with Sample Complexity:
TR00-086 | 26th September 2000
Michael Schmitt

On the Complexity of Computing and Learning with Multiplicative Neural Networks

In a great variety of neuron models neural inputs are
combined using the summing operation. We introduce the concept of
multiplicative neural networks which contain units that multiply
their inputs instead of summing them and, thus, allow inputs to
interact nonlinearly. The class of multiplicative networks
comprises such widely known ... more >>>

TR01-100 | 14th December 2001
Noga Alon, Wenceslas Fernandez de la Vega, Ravi Kannan, Marek Karpinski

Random Sampling and Approximation of MAX-CSP Problems

We present a new efficient sampling method for approximating
r-dimensional Maximum Constraint Satisfaction Problems, MAX-rCSP, on
n variables up to an additive error \epsilon n^r.We prove a new
general paradigm in that it suffices, for a given set of constraints,
to pick a small uniformly random ... more >>>

TR06-104 | 25th August 2006
Wenceslas Fernandez de la Vega, Marek Karpinski

On the Sample Complexity of MAX-CUT

We give a simple proof for the sample complexity bound $O~(1/\epsilon^4)$ of absolute approximation of MAX-CUT. The proof depends on a new analysis method for linear programs (LPs) underlying MAX-CUT which could be also of independent interest.

more >>>

TR06-106 | 18th August 2006
Scott Aaronson

The Learnability of Quantum States

Traditional quantum state tomography requires a number of measurements that grows exponentially with the number of qubits n. But using ideas from computational learning theory, we show that "for most practical purposes" one can learn a state using a number of measurements that grows only linearly with n. Besides possible ... more >>>

TR06-124 | 25th September 2006
Wenceslas Fernandez de la Vega, Ravi Kannan, Marek Karpinski

Approximation of Global MAX-CSP Problems

We study the problem of absolute approximability of MAX-CSP problems with the global constraints. We prove existence of an efficient sampling method for the MAX-CSP class of problems with linear global constraints and bounded feasibility gap. It gives for the first time a polynomial in epsilon^-1 sample complexity bound for ... more >>>

TR13-111 | 17th August 2013
Gregory Valiant, Paul Valiant

Instance-by-instance optimal identity testing

We consider the problem of verifying the identity of a distribution: Given the description of a distribution over a discrete support $p=(p_1,p_2,\ldots,p_n)$, how many samples (independent draws) must one obtain from an unknown distribution, $q$, to distinguish, with high probability, the case that $p=q$ from the case that the total ... more >>>

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