3,368 research outputs found
Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization
Creating impact in real-world settings requires artificial intelligence
techniques to span the full pipeline from data, to predictive models, to
decisions. These components are typically approached separately: a machine
learning model is first trained via a measure of predictive accuracy, and then
its predictions are used as input into an optimization algorithm which produces
a decision. However, the loss function used to train the model may easily be
misaligned with the end goal, which is to make the best decisions possible.
Hand-tuning the loss function to align with optimization is a difficult and
error-prone process (which is often skipped entirely).
We focus on combinatorial optimization problems and introduce a general
framework for decision-focused learning, where the machine learning model is
directly trained in conjunction with the optimization algorithm to produce
high-quality decisions. Technically, our contribution is a means of integrating
common classes of discrete optimization problems into deep learning or other
predictive models, which are typically trained via gradient descent. The main
idea is to use a continuous relaxation of the discrete problem to propagate
gradients through the optimization procedure. We instantiate this framework for
two broad classes of combinatorial problems: linear programs and submodular
maximization. Experimental results across a variety of domains show that
decision-focused learning often leads to improved optimization performance
compared to traditional methods. We find that standard measures of accuracy are
not a reliable proxy for a predictive model's utility in optimization, and our
method's ability to specify the true goal as the model's training objective
yields substantial dividends across a range of decision problems.Comment: Full version of paper accepted at AAAI 201
Detecting Strong Ties Using Network Motifs
Detecting strong ties among users in social and information networks is a
fundamental operation that can improve performance on a multitude of
personalization and ranking tasks. Strong-tie edges are often readily obtained
from the social network as users often participate in multiple overlapping
networks via features such as following and messaging. These networks may vary
greatly in size, density and the information they carry. This setting leads to
a natural strong tie detection task: given a small set of labeled strong tie
edges, how well can one detect unlabeled strong ties in the remainder of the
network?
This task becomes particularly daunting for the Twitter network due to scant
availability of pairwise relationship attribute data, and sparsity of strong
tie networks such as phone contacts. Given these challenges, a natural approach
is to instead use structural network features for the task, produced by {\em
combining} the strong and "weak" edges. In this work, we demonstrate via
experiments on Twitter data that using only such structural network features is
sufficient for detecting strong ties with high precision. These structural
network features are obtained from the presence and frequency of small network
motifs on combined strong and weak ties. We observe that using motifs larger
than triads alleviate sparsity problems that arise for smaller motifs, both due
to increased combinatorial possibilities as well as benefiting strongly from
searching beyond the ego network. Empirically, we observe that not all motifs
are equally useful, and need to be carefully constructed from the combined
edges in order to be effective for strong tie detection. Finally, we reinforce
our experimental findings with providing theoretical justification that
suggests why incorporating these larger sized motifs as features could lead to
increased performance in planted graph models.Comment: To appear in Proceedings of WWW 2017 (Web-science track
Recommended from our members
Optimal Policy Derivation for Transmission Duty-Cycle Constrained LPWAN
Low-power wide-area network (LPWAN) technologies enable Internet of Things (IoT) devices to efficiently and robustly communicate over long distances, thus making them especially suited for industrial environments. However, the stringent regulations on the usage of certain industrial, scientific, and medical bands in many countries in which LPWAN operate limit the amount of time IoT motes can occupy the shared bands. This is particularly challenging in industrial scenarios, where not being able to report some detected events might result in the failure of critical assets. To alleviate this, and by mathematically modeling LPWAN-based IoT motes, we have derived optimal transmission policies that maximize the number of reported events (prioritized by their importance) while still complying with current regulations. The proposed solution has been customized for two widely known LPWAN technologies: 1) LoRa and 2) Sigfox. Analytical results reveal that our solution is feasible and performs remarkably close to the theoretical limit for a wide range of network activity patterns
- …