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HyperFair: A Soft Approach to Integrating Fairness Criteria
Recommender systems are being employed across an increasingly diverse se...
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Causal Relational Learning
Causal inference is at the heart of empirical research in natural and so...
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Estimating Aggregate Properties In Relational Networks With Unobserved Data
Aggregate network properties such as cluster cohesion and the number of ...
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User Profiling Using Hinge-loss Markov Random Fields
A variety of approaches have been proposed to automatically infer the pr...
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Estimating Causal Effects of Tone in Online Debates
Statistical methods applied to social media posts shed light on the dyna...
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SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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A Fairness-aware Hybrid Recommender System
Recommender systems are used in variety of domains affecting people's li...
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Scalable Structure Learning for Probabilistic Soft Logic
Statistical relational frameworks such as Markov logic networks and prob...
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Using Noisy Extractions to Discover Causal Knowledge
Knowledge bases (KB) constructed through information extraction from tex...
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Generic Statistical Relational Entity Resolution in Knowledge Graphs
Entity resolution, the problem of identifying the underlying entity of r...
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Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks
A neighborhood graph, which represents the instances as vertices and the...
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Hinge-Loss Markov Random Fields and Probabilistic Soft Logic
A fundamental challenge in developing high-impact machine learning techn...
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Value of Information Lattice: Exploiting Probabilistic Independence for Effective Feature Subset Acquisition
We address the cost-sensitive feature acquisition problem, where misclas...
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Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction
Graphical models for structured domains are powerful tools, but the comp...
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A Hypergraph-Partitioned Vertex Programming Approach for Large-scale Consensus Optimization
In modern data science problems, techniques for extracting value from bi...
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Scalable Text and Link Analysis with Mixed-Topic Link Models
Many data sets contain rich information about objects, as well as pairwi...
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Utility Elicitation as a Classification Problem
We investigate the application of classification techniques to utility e...
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Bisimulation-based Approximate Lifted Inference
There has been a great deal of recent interest in methods for performing...
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Probabilistic Similarity Logic
Many machine learning applications require the ability to learn from and...
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Lifted Graphical Models: A Survey
This article presents a survey of work on lifted graphical models. We re...
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A Probabilistic Approach for Learning Folksonomies from Structured Data
Learning structured representations has emerged as an important problem ...
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Growing a Tree in the Forest: Constructing Folksonomies by Integrating Structured Metadata
Many social Web sites allow users to annotate the content with descripti...
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Integrating Structured Metadata with Relational Affinity Propagation
Structured and semi-structured data describing entities, taxonomies and ...
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