18,292 research outputs found
Learning from Neighbors about a Changing State
Agents learn about a changing state using private signals and past actions of
neighbors in a network. We characterize equilibrium learning and social
influence in this setting. We then examine when agents can aggregate
information well, responding quickly to recent changes. A key sufficient
condition for good aggregation is that each individual's neighbors have
sufficiently different types of private information. In contrast, when signals
are homogeneous, aggregation is suboptimal on any network. We also examine
behavioral versions of the model, and show that achieving good aggregation
requires a sophisticated understanding of correlations in neighbors' actions.
The model provides a Bayesian foundation for a tractable learning dynamic in
networks, closely related to the DeGroot model, and offers new tools for
counterfactual and welfare analyses.Comment: minor revision tweaking exposition relative to v5 - which added new
Section 3.2.2, new Theorem 2, new Section 7.1, many local revision
Nonparametric Bayes dynamic modeling of relational data
Symmetric binary matrices representing relations among entities are commonly
collected in many areas. Our focus is on dynamically evolving binary relational
matrices, with interest being in inference on the relationship structure and
prediction. We propose a nonparametric Bayesian dynamic model, which reduces
dimensionality in characterizing the binary matrix through a lower-dimensional
latent space representation, with the latent coordinates evolving in continuous
time via Gaussian processes. By using a logistic mapping function from the
probability matrix space to the latent relational space, we obtain a flexible
and computational tractable formulation. Employing P\`olya-Gamma data
augmentation, an efficient Gibbs sampler is developed for posterior
computation, with the dimension of the latent space automatically inferred. We
provide some theoretical results on flexibility of the model, and illustrate
performance via simulation experiments. We also consider an application to
co-movements in world financial markets
A Survey of Location Prediction on Twitter
Locations, e.g., countries, states, cities, and point-of-interests, are
central to news, emergency events, and people's daily lives. Automatic
identification of locations associated with or mentioned in documents has been
explored for decades. As one of the most popular online social network
platforms, Twitter has attracted a large number of users who send millions of
tweets on daily basis. Due to the world-wide coverage of its users and
real-time freshness of tweets, location prediction on Twitter has gained
significant attention in recent years. Research efforts are spent on dealing
with new challenges and opportunities brought by the noisy, short, and
context-rich nature of tweets. In this survey, we aim at offering an overall
picture of location prediction on Twitter. Specifically, we concentrate on the
prediction of user home locations, tweet locations, and mentioned locations. We
first define the three tasks and review the evaluation metrics. By summarizing
Twitter network, tweet content, and tweet context as potential inputs, we then
structurally highlight how the problems depend on these inputs. Each dependency
is illustrated by a comprehensive review of the corresponding strategies
adopted in state-of-the-art approaches. In addition, we also briefly review two
related problems, i.e., semantic location prediction and point-of-interest
recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur
Network Inference via the Time-Varying Graphical Lasso
Many important problems can be modeled as a system of interconnected
entities, where each entity is recording time-dependent observations or
measurements. In order to spot trends, detect anomalies, and interpret the
temporal dynamics of such data, it is essential to understand the relationships
between the different entities and how these relationships evolve over time. In
this paper, we introduce the time-varying graphical lasso (TVGL), a method of
inferring time-varying networks from raw time series data. We cast the problem
in terms of estimating a sparse time-varying inverse covariance matrix, which
reveals a dynamic network of interdependencies between the entities. Since
dynamic network inference is a computationally expensive task, we derive a
scalable message-passing algorithm based on the Alternating Direction Method of
Multipliers (ADMM) to solve this problem in an efficient way. We also discuss
several extensions, including a streaming algorithm to update the model and
incorporate new observations in real time. Finally, we evaluate our TVGL
algorithm on both real and synthetic datasets, obtaining interpretable results
and outperforming state-of-the-art baselines in terms of both accuracy and
scalability
Dynamic Poisson Factorization
Models for recommender systems use latent factors to explain the preferences
and behaviors of users with respect to a set of items (e.g., movies, books,
academic papers). Typically, the latent factors are assumed to be static and,
given these factors, the observed preferences and behaviors of users are
assumed to be generated without order. These assumptions limit the explorative
and predictive capabilities of such models, since users' interests and item
popularity may evolve over time. To address this, we propose dPF, a dynamic
matrix factorization model based on the recent Poisson factorization model for
recommendations. dPF models the time evolving latent factors with a Kalman
filter and the actions with Poisson distributions. We derive a scalable
variational inference algorithm to infer the latent factors. Finally, we
demonstrate dPF on 10 years of user click data from arXiv.org, one of the
largest repository of scientific papers and a formidable source of information
about the behavior of scientists. Empirically we show performance improvement
over both static and, more recently proposed, dynamic recommendation models. We
also provide a thorough exploration of the inferred posteriors over the latent
variables.Comment: RecSys 201
DOC: Deep Open Classification of Text Documents
Traditional supervised learning makes the closed-world assumption that the
classes appeared in the test data must have appeared in training. This also
applies to text learning or text classification. As learning is used
increasingly in dynamic open environments where some new/test documents may not
belong to any of the training classes, identifying these novel documents during
classification presents an important problem. This problem is called open-world
classification or open classification. This paper proposes a novel deep
learning based approach. It outperforms existing state-of-the-art techniques
dramatically.Comment: accepted at EMNLP 201
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