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The problem of attributing a deep network’s prediction to its input/base features is
well-studied (cf. Simonyan et al. (2013)). We introduce the notion of conductance
to extend the notion of attribution to understanding the importance of hidden units.
Informally, the conductance of a hidden unit of a deep network is the flow of attribution
via this hidden unit. We can use conductance to understand the importance of
a hidden unit to the prediction for a specific input, or over a set of inputs. We justify
conductance in multiple ways via a qualitative comparison with other methods,
via some axiomatic results, and via an empirical evaluation based on a feature
selection task. The empirical evaluations are done using the Inception network
over ImageNet data, and a convolutinal network over text data. In both cases, we
demonstrate the effectiveness of conductance in identifying interesting insights
about the internal workings of these networks.
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Analyza: Exploring Data with Conversation
Kevin McCurley
Ralfi Nahmias
Intelligent User Interfaces 2017, ACM, Limassol, Cyprus (to appear)
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We describe Analyza, a system that helps lay users explore
data. Analyza has been used within two large real world systems. The
first is a question-and-answer feature in a spreadsheet product. The
second provides convenient access to a revenue/inventory database
for a large sales force. Both user bases consist of users who do not
necessarily have coding skills, demonstrating Analyza's ability to
democratize access to data.
We discuss the key design decisions in implementing this system.
For instance, how to mix structured and natural language modalities,
how to use conversation to disambiguate and simplify querying, how
to rely on the ``semantics'' of the data to compensate for the lack
of syntactic structure, and how to efficiently curate the data.
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Whole-page optimization and submodular welfare maximization with online bidders
Nikhil Devanur
Zhiyi Huang
ACM Conference on Electronic Commerce (EC) 2013, pp. 305-322
Preview abstract
In the context of online ad serving, display ads may appear on different types of webpages, where each page includes several ad slots and therefore multiple ads can be shown on each page. The set of ads that can be assigned to ad slots of the same page needs to satisfy various prespecified constraints including exclusion constraints, diversity constraints, and the like. Upon arrival of a user, the ad serving system needs to allocate a set of ads to the current webpage respecting these per-page allocation constraints. Previous slot-based settings ignore the important concept of a page and may lead to highly suboptimal results in general. In this article, motivated by these applications in display advertising and inspired by the submodular welfare maximization problem with online bidders, we study a general class of page-based ad allocation problems, present the first (tight) constant-factor approximation algorithms for these problems, and confirm the performance of our algorithms experimentally on real-world datasets.
A key technical ingredient of our results is a novel primal-dual analysis for handling free disposal, which updates dual variables using a “level function” instead of a single level and unifies with previous analyses of related problems. This new analysis method allows us to handle arbitrarily complicated allocation constraints for each page. Our main result is an algorithm that achieves a 1 &minus frac 1 e &minus o(1)-competitive ratio. Moreover, our experiments on real-world datasets show significant improvements of our page-based algorithms compared to the slot-based algorithms.
Finally, we observe that our problem is closely related to the submodular welfare maximization (SWM) problem. In particular, we introduce a variant of the SWM problem with online bidders and show how to solve this problem using our algorithm for whole-page optimization.
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