53,325 research outputs found
Classification with Costly Features using Deep Reinforcement Learning
We study a classification problem where each feature can be acquired for a
cost and the goal is to optimize a trade-off between the expected
classification error and the feature cost. We revisit a former approach that
has framed the problem as a sequential decision-making problem and solved it by
Q-learning with a linear approximation, where individual actions are either
requests for feature values or terminate the episode by providing a
classification decision. On a set of eight problems, we demonstrate that by
replacing the linear approximation with neural networks the approach becomes
comparable to the state-of-the-art algorithms developed specifically for this
problem. The approach is flexible, as it can be improved with any new
reinforcement learning enhancement, it allows inclusion of pre-trained
high-performance classifier, and unlike prior art, its performance is robust
across all evaluated datasets.Comment: AAAI 201
ActiveRemediation: The Search for Lead Pipes in Flint, Michigan
We detail our ongoing work in Flint, Michigan to detect pipes made of lead
and other hazardous metals. After elevated levels of lead were detected in
residents' drinking water, followed by an increase in blood lead levels in area
children, the state and federal governments directed over $125 million to
replace water service lines, the pipes connecting each home to the water
system. In the absence of accurate records, and with the high cost of
determining buried pipe materials, we put forth a number of predictive and
procedural tools to aid in the search and removal of lead infrastructure.
Alongside these statistical and machine learning approaches, we describe our
interactions with government officials in recommending homes for both
inspection and replacement, with a focus on the statistical model that adapts
to incoming information. Finally, in light of discussions about increased
spending on infrastructure development by the federal government, we explore
how our approach generalizes beyond Flint to other municipalities nationwide.Comment: 10 pages, 10 figures, To appear in KDD 2018, For associated
promotional video, see https://www.youtube.com/watch?v=YbIn_axYu9
Optimal Structuring of Assessment Processes in Competition Law: A Survey of Theoretical Approaches
In competition law, the problem of the optimal design of institutional and procedural rules concerns assessment processes of the pro- and anticompetitiveness of business behaviors. This is well recognized in the discussion about the relative merits of different assessment principles such as the rule of reason and per se rules. Supported by modern industrial organization research, which applies a more differentiated analysis to the welfare effects of different business behaviors, a full-scale case-by-case assessment seems to be the prevailing idea. Even though the discussion mainly focuses on extreme solutions, different theoretical approaches do exist, which provide important determinants and allow for a sound analysis of appropriate legal directives and investigation procedures from a âLaw and Economicsâ perspective. Integrating and examining them in light of various constellations results in differentiated solutions of optimally structured assessment processes.Law Enforcement, Competition Law, Competition Policy, Antitrust Law, Antitrust Policy, Decision-Making
Resource Constrained Structured Prediction
We study the problem of structured prediction under test-time budget
constraints. We propose a novel approach applicable to a wide range of
structured prediction problems in computer vision and natural language
processing. Our approach seeks to adaptively generate computationally costly
features during test-time in order to reduce the computational cost of
prediction while maintaining prediction performance. We show that training the
adaptive feature generation system can be reduced to a series of structured
learning problems, resulting in efficient training using existing structured
learning algorithms. This framework provides theoretical justification for
several existing heuristic approaches found in literature. We evaluate our
proposed adaptive system on two structured prediction tasks, optical character
recognition (OCR) and dependency parsing and show strong performance in
reduction of the feature costs without degrading accuracy
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