17 research outputs found
Non-adaptive Group Testing on Graphs
Grebinski and Kucherov (1998) and Alon et al. (2004-2005) study the problem
of learning a hidden graph for some especial cases, such as hamiltonian cycle,
cliques, stars, and matchings. This problem is motivated by problems in
chemical reactions, molecular biology and genome sequencing.
In this paper, we present a generalization of this problem. Precisely, we
consider a graph G and a subgraph H of G and we assume that G contains exactly
one defective subgraph isomorphic to H. The goal is to find the defective
subgraph by testing whether an induced subgraph contains an edge of the
defective subgraph, with the minimum number of tests. We present an upper bound
for the number of tests to find the defective subgraph by using the symmetric
and high probability variation of Lov\'asz Local Lemma
On Multistage Learning a Hidden Hypergraph
Learning a hidden hypergraph is a natural generalization of the classical
group testing problem that consists in detecting unknown hypergraph
by carrying out edge-detecting tests. In the given paper we
focus our attention only on a specific family of localized
hypergraphs for which the total number of vertices , the number of
edges , , and the cardinality of any edge ,
. Our goal is to identify all edges of by
using the minimal number of tests. We develop an adaptive algorithm that
matches the information theory bound, i.e., the total number of tests of the
algorithm in the worst case is at most . We also discuss
a probabilistic generalization of the problem.Comment: 5 pages, IEEE conferenc
Optimal Query Complexity for Reconstructing Hypergraphs
In this paper we consider the problem of reconstructing a hidden weighted
hypergraph of constant rank using additive queries. We prove the following: Let
be a weighted hidden hypergraph of constant rank with n vertices and
hyperedges. For any there exists a non-adaptive algorithm that finds the
edges of the graph and their weights using
additive queries. This solves the open problem in [S. Choi, J. H. Kim. Optimal
Query Complexity Bounds for Finding Graphs. {\em STOC}, 749--758,~2008].
When the weights of the hypergraph are integers that are less than
where is the rank of the hypergraph (and therefore for
unweighted hypergraphs) there exists a non-adaptive algorithm that finds the
edges of the graph and their weights using additive queries.
Using the information theoretic bound the above query complexities are tight
Learning Boolean Halfspaces with Small Weights from Membership Queries
We consider the problem of proper learning a Boolean Halfspace with integer
weights from membership queries only. The best known
algorithm for this problem is an adaptive algorithm that asks
membership queries where the best lower bound for the number of membership
queries is [Learning Threshold Functions with Small Weights Using
Membership Queries. COLT 1999]
In this paper we close this gap and give an adaptive proper learning
algorithm with two rounds that asks membership queries. We also give
a non-adaptive proper learning algorithm that asks membership
queries