30,754 research outputs found
Experiences in Bayesian Inference in Baltic Salmon Management
We review a success story regarding Bayesian inference in fisheries
management in the Baltic Sea. The management of salmon fisheries is currently
based on the results of a complex Bayesian population dynamic model, and
managers and stakeholders use the probabilities in their discussions. We also
discuss the technical and human challenges in using Bayesian modeling to give
practical advice to the public and to government officials and suggest future
areas in which it can be applied. In particular, large databases in fisheries
science offer flexible ways to use hierarchical models to learn the population
dynamics parameters for those by-catch species that do not have similar large
stock-specific data sets like those that exist for many target species. This
information is required if we are to understand the future ecosystem risks of
fisheries.Comment: Published in at http://dx.doi.org/10.1214/13-STS431 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Online Learning with Low Rank Experts
We consider the problem of prediction with expert advice when the losses of
the experts have low-dimensional structure: they are restricted to an unknown
-dimensional subspace. We devise algorithms with regret bounds that are
independent of the number of experts and depend only on the rank . For the
stochastic model we show a tight bound of , and extend it to
a setting of an approximate subspace. For the adversarial model we show an
upper bound of and a lower bound of
Active Learning with Expert Advice
Conventional learning with expert advice methods assumes a learner is always
receiving the outcome (e.g., class labels) of every incoming training instance
at the end of each trial. In real applications, acquiring the outcome from
oracle can be costly or time consuming. In this paper, we address a new problem
of active learning with expert advice, where the outcome of an instance is
disclosed only when it is requested by the online learner. Our goal is to learn
an accurate prediction model by asking the oracle the number of questions as
small as possible. To address this challenge, we propose a framework of active
forecasters for online active learning with expert advice, which attempts to
extend two regular forecasters, i.e., Exponentially Weighted Average Forecaster
and Greedy Forecaster, to tackle the task of active learning with expert
advice. We prove that the proposed algorithms satisfy the Hannan consistency
under some proper assumptions, and validate the efficacy of our technique by an
extensive set of experiments.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty
in Artificial Intelligence (UAI2013
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
Coordinating agents to complete a set of tasks with intercoupled temporal and
resource constraints is computationally challenging, yet human domain experts
can solve these difficult scheduling problems using paradigms learned through
years of apprenticeship. A process for manually codifying this domain knowledge
within a computational framework is necessary to scale beyond the
``single-expert, single-trainee" apprenticeship model. However, human domain
experts often have difficulty describing their decision-making processes,
causing the codification of this knowledge to become laborious. We propose a
new approach for capturing domain-expert heuristics through a pairwise ranking
formulation. Our approach is model-free and does not require enumerating or
iterating through a large state space. We empirically demonstrate that this
approach accurately learns multifaceted heuristics on a synthetic data set
incorporating job-shop scheduling and vehicle routing problems, as well as on
two real-world data sets consisting of demonstrations of experts solving a
weapon-to-target assignment problem and a hospital resource allocation problem.
We also demonstrate that policies learned from human scheduling demonstration
via apprenticeship learning can substantially improve the efficiency of a
branch-and-bound search for an optimal schedule. We employ this human-machine
collaborative optimization technique on a variant of the weapon-to-target
assignment problem. We demonstrate that this technique generates solutions
substantially superior to those produced by human domain experts at a rate up
to 9.5 times faster than an optimization approach and can be applied to
optimally solve problems twice as complex as those solved by a human
demonstrator.Comment: Portions of this paper were published in the Proceedings of the
International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and
in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper
consists of 50 pages with 11 figures and 4 table
An Entropy Search Portfolio for Bayesian Optimization
Bayesian optimization is a sample-efficient method for black-box global
optimization. How- ever, the performance of a Bayesian optimization method very
much depends on its exploration strategy, i.e. the choice of acquisition
function, and it is not clear a priori which choice will result in superior
performance. While portfolio methods provide an effective, principled way of
combining a collection of acquisition functions, they are often based on
measures of past performance which can be misleading. To address this issue, we
introduce the Entropy Search Portfolio (ESP): a novel approach to portfolio
construction which is motivated by information theoretic considerations. We
show that ESP outperforms existing portfolio methods on several real and
synthetic problems, including geostatistical datasets and simulated control
tasks. We not only show that ESP is able to offer performance as good as the
best, but unknown, acquisition function, but surprisingly it often gives better
performance. Finally, over a wide range of conditions we find that ESP is
robust to the inclusion of poor acquisition functions.Comment: 10 pages, 5 figure
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