112,694 research outputs found
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
Optimal Net-Load Balancing in Smart Grids with High PV Penetration
Mitigating Supply-Demand mismatch is critical for smooth power grid
operation. Traditionally, load curtailment techniques such as Demand Response
(DR) have been used for this purpose. However, these cannot be the only
component of a net-load balancing framework for Smart Grids with high PV
penetration. These grids can sometimes exhibit supply surplus causing
over-voltages. Supply curtailment techniques such as Volt-Var Optimizations are
complex and computationally expensive. This increases the complexity of
net-load balancing systems used by the grid operator and limits their
scalability. Recently new technologies have been developed that enable the
rapid and selective connection of PV modules of an installation to the grid.
Taking advantage of these advancements, we develop a unified optimal net-load
balancing framework which performs both load and solar curtailment. We show
that when the available curtailment values are discrete, this problem is
NP-hard and develop bounded approximation algorithms for minimizing the
curtailment cost. Our algorithms produce fast solutions, given the tight timing
constraints required for grid operation. We also incorporate the notion of
fairness to ensure that curtailment is evenly distributed among all the nodes.
Finally, we develop an online algorithm which performs net-load balancing using
only data available for the current interval. Using both theoretical analysis
and practical evaluations, we show that our net-load balancing algorithms
provide solutions which are close to optimal in a small amount of time.Comment: 11 pages. To be published in the 4th ACM International Conference on
Systems for Energy-Efficient Built Environments (BuildSys 17) Changes from
previous version: Fixed a bug in Algorithm 1 which was causing some min cost
solutions to be misse
Online Model Evaluation in a Large-Scale Computational Advertising Platform
Online media provides opportunities for marketers through which they can
deliver effective brand messages to a wide range of audiences. Advertising
technology platforms enable advertisers to reach their target audience by
delivering ad impressions to online users in real time. In order to identify
the best marketing message for a user and to purchase impressions at the right
price, we rely heavily on bid prediction and optimization models. Even though
the bid prediction models are well studied in the literature, the equally
important subject of model evaluation is usually overlooked. Effective and
reliable evaluation of an online bidding model is crucial for making faster
model improvements as well as for utilizing the marketing budgets more
efficiently. In this paper, we present an experimentation framework for bid
prediction models where our focus is on the practical aspects of model
evaluation. Specifically, we outline the unique challenges we encounter in our
platform due to a variety of factors such as heterogeneous goal definitions,
varying budget requirements across different campaigns, high seasonality and
the auction-based environment for inventory purchasing. Then, we introduce
return on investment (ROI) as a unified model performance (i.e., success)
metric and explain its merits over more traditional metrics such as
click-through rate (CTR) or conversion rate (CVR). Most importantly, we discuss
commonly used evaluation and metric summarization approaches in detail and
propose a more accurate method for online evaluation of new experimental models
against the baseline. Our meta-analysis-based approach addresses various
shortcomings of other methods and yields statistically robust conclusions that
allow us to conclude experiments more quickly in a reliable manner. We
demonstrate the effectiveness of our evaluation strategy on real campaign data
through some experiments.Comment: Accepted to ICDM201
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