353 research outputs found
Bayesian Incentive Compatibility via Fractional Assignments
Very recently, Hartline and Lucier studied single-parameter mechanism design
problems in the Bayesian setting. They proposed a black-box reduction that
converted Bayesian approximation algorithms into Bayesian-Incentive-Compatible
(BIC) mechanisms while preserving social welfare. It remains a major open
question if one can find similar reduction in the more important
multi-parameter setting. In this paper, we give positive answer to this
question when the prior distribution has finite and small support. We propose a
black-box reduction for designing BIC multi-parameter mechanisms. The reduction
converts any algorithm into an eps-BIC mechanism with only marginal loss in
social welfare. As a result, for combinatorial auctions with sub-additive
agents we get an eps-BIC mechanism that achieves constant approximation.Comment: 22 pages, 1 figur
Exploiting Metric Structure for Efficient Private Query Release
We consider the problem of privately answering queries defined on databases
which are collections of points belonging to some metric space. We give simple,
computationally efficient algorithms for answering distance queries defined
over an arbitrary metric. Distance queries are specified by points in the
metric space, and ask for the average distance from the query point to the
points contained in the database, according to the specified metric. Our
algorithms run efficiently in the database size and the dimension of the space,
and operate in both the online query release setting, and the offline setting
in which they must in polynomial time generate a fixed data structure which can
answer all queries of interest. This represents one of the first subclasses of
linear queries for which efficient algorithms are known for the private query
release problem, circumventing known hardness results for generic linear
queries
Making the Most of Your Samples
We study the problem of setting a price for a potential buyer with a
valuation drawn from an unknown distribution . The seller has "data"' about
in the form of i.i.d. samples, and the algorithmic challenge is
to use these samples to obtain expected revenue as close as possible to what
could be achieved with advance knowledge of .
Our first set of results quantifies the number of samples that are
necessary and sufficient to obtain a -approximation. For example,
for an unknown distribution that satisfies the monotone hazard rate (MHR)
condition, we prove that samples are
necessary and sufficient. Remarkably, this is fewer samples than is necessary
to accurately estimate the expected revenue obtained by even a single reserve
price. We also prove essentially tight sample complexity bounds for regular
distributions, bounded-support distributions, and a wide class of irregular
distributions. Our lower bound approach borrows tools from differential privacy
and information theory, and we believe it could find further applications in
auction theory.
Our second set of results considers the single-sample case. For regular
distributions, we prove that no pricing strategy is better than
-approximate, and this is optimal by the Bulow-Klemperer theorem.
For MHR distributions, we show how to do better: we give a simple pricing
strategy that guarantees expected revenue at least times the maximum
possible. We also prove that no pricing strategy achieves an approximation
guarantee better than
Multi-disk subsystem organizations for very large databases
This thesis investigates efficient mappings of very large databases with non-uniform access to its data. to a. multi-disk subsystem.
Two algorithms are developed to distribute the database across multiple disks, possibly with replication, in order to minimize latency and maximize throughput. These algorithms are compared with respect to the amount of replication overhead incurred to achieve desired throughput.
A simulator is developed to simulate these two mapping algorithms and investigate the efficiency of these two mappings
The Sample Complexity of Auctions with Side Information
Traditionally, the Bayesian optimal auction design problem has been
considered either when the bidder values are i.i.d, or when each bidder is
individually identifiable via her value distribution. The latter is a
reasonable approach when the bidders can be classified into a few categories,
but there are many instances where the classification of bidders is a
continuum. For example, the classification of the bidders may be based on their
annual income, their propensity to buy an item based on past behavior, or in
the case of ad auctions, the click through rate of their ads. We introduce an
alternate model that captures this aspect, where bidders are a priori
identical, but can be distinguished based (only) on some side information the
auctioneer obtains at the time of the auction. We extend the sample complexity
approach of Dhangwatnotai et al. and Cole and Roughgarden to this model and
obtain almost matching upper and lower bounds. As an aside, we obtain a revenue
monotonicity lemma which may be of independent interest. We also show how to
use Empirical Risk Minimization techniques to improve the sample complexity
bound of Cole and Roughgarden for the non-identical but independent value
distribution case.Comment: A version of this paper appeared in STOC 201
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