Hal Wasserman

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We describe a slightly subexponential time algorithm for learning parity functions in the presence of random classification noise, a problem closely related to several cryptographic and coding problems. Our algorithm runs in polynomial time for the case of parity functions that depend on only the first <i>O</i>(log <i>n</i> log log <i>n</i>) bits of input,(More)
To take advantage of the full potential of ubiquitous computing, we will need systems which minimize powerconsumption. Weiser et al. and others have suggested that this may be accomplished by a CPU which dynamically changes speed and voltage, thereby saving energy by spreading run cycles into idle time. Here we continue this research, using a simulation to(More)
C?cneralizing the high-noise decoding methods of [I, 191 to the class of algebraic-geometric codes, we design the first polynomialtime algorithms to decode algebraic-geometric codes significantly beyond the conventional error-correction bound. Applying our results to codes obtained from curves with many rational points, we construct arbitrarily long,(More)
Sudan and others have considered the problem of reconstructing a bounded-degree polynomial f : F” + F from n data-points, only t of which are guaranteed to be consistent with f. For t << n/2, the solution may not be unique; but it may be possible to find a small set of candidates for f. Here we extend this work, proving results including the following: Pick(More)