6,408 research outputs found
Active classification with comparison queries
We study an extension of active learning in which the learning algorithm may
ask the annotator to compare the distances of two examples from the boundary of
their label-class. For example, in a recommendation system application (say for
restaurants), the annotator may be asked whether she liked or disliked a
specific restaurant (a label query); or which one of two restaurants did she
like more (a comparison query).
We focus on the class of half spaces, and show that under natural
assumptions, such as large margin or bounded bit-description of the input
examples, it is possible to reveal all the labels of a sample of size using
approximately queries. This implies an exponential improvement over
classical active learning, where only label queries are allowed. We complement
these results by showing that if any of these assumptions is removed then, in
the worst case, queries are required.
Our results follow from a new general framework of active learning with
additional queries. We identify a combinatorial dimension, called the
\emph{inference dimension}, that captures the query complexity when each
additional query is determined by examples (such as comparison queries,
each of which is determined by the two compared examples). Our results for half
spaces follow by bounding the inference dimension in the cases discussed above.Comment: 23 pages (not including references), 1 figure. The new version
contains a minor fix in the proof of Lemma 4.
Learning Convex Partitions and Computing Game-theoretic Equilibria from Best Response Queries
Suppose that an -simplex is partitioned into convex regions having
disjoint interiors and distinct labels, and we may learn the label of any point
by querying it. The learning objective is to know, for any point in the
simplex, a label that occurs within some distance from that point.
We present two algorithms for this task: Constant-Dimension Generalised Binary
Search (CD-GBS), which for constant uses queries, and Constant-Region Generalised Binary
Search (CR-GBS), which uses CD-GBS as a subroutine and for constant uses
queries.
We show via Kakutani's fixed-point theorem that these algorithms provide
bounds on the best-response query complexity of computing approximate
well-supported equilibria of bimatrix games in which one of the players has a
constant number of pure strategies. We also partially extend our results to
games with multiple players, establishing further query complexity bounds for
computing approximate well-supported equilibria in this setting.Comment: 38 pages, 7 figures, second version strengthens lower bound in
Theorem 6, adds footnotes with additional comments and fixes typo
Deterministic and Probabilistic Binary Search in Graphs
We consider the following natural generalization of Binary Search: in a given
undirected, positively weighted graph, one vertex is a target. The algorithm's
task is to identify the target by adaptively querying vertices. In response to
querying a node , the algorithm learns either that is the target, or is
given an edge out of that lies on a shortest path from to the target.
We study this problem in a general noisy model in which each query
independently receives a correct answer with probability (a
known constant), and an (adversarial) incorrect one with probability .
Our main positive result is that when (i.e., all answers are
correct), queries are always sufficient. For general , we give an
(almost information-theoretically optimal) algorithm that uses, in expectation,
no more than queries, and identifies the target correctly with probability at
leas . Here, denotes the
entropy. The first bound is achieved by the algorithm that iteratively queries
a 1-median of the nodes not ruled out yet; the second bound by careful repeated
invocations of a multiplicative weights algorithm.
Even for , we show several hardness results for the problem of
determining whether a target can be found using queries. Our upper bound of
implies a quasipolynomial-time algorithm for undirected connected
graphs; we show that this is best-possible under the Strong Exponential Time
Hypothesis (SETH). Furthermore, for directed graphs, or for undirected graphs
with non-uniform node querying costs, the problem is PSPACE-complete. For a
semi-adaptive version, in which one may query nodes each in rounds, we
show membership in in the polynomial hierarchy, and hardness
for
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
The geometry of quantum learning
Concept learning provides a natural framework in which to place the problems
solved by the quantum algorithms of Bernstein-Vazirani and Grover. By combining
the tools used in these algorithms--quantum fast transforms and amplitude
amplification--with a novel (in this context) tool--a solution method for
geometrical optimization problems--we derive a general technique for quantum
concept learning. We name this technique "Amplified Impatient Learning" and
apply it to construct quantum algorithms solving two new problems: BATTLESHIP
and MAJORITY, more efficiently than is possible classically.Comment: 20 pages, plain TeX with amssym.tex, related work at
http://www.math.uga.edu/~hunziker/ and http://math.ucsd.edu/~dmeyer
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