David Poole

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The University of British Columbia

Binarised Regression with Instance-Varying Costs: Evaluation using Impact Curves

Many evaluation methods exist, each for a particular prediction task, an...
08/14/2020 ∙ by Matthew Dirks, et al. ∙ 0 ∙ share

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A Probabilistic Approach for Predicting Landslides by Learning a Self-Aligned Deep Convolutional Model

Landslides are movement of soil and rock under the influence of gravity....
11/12/2019 ∙ by Ainaz Hajimoradlou, et al. ∙ 30 ∙ share

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Knowledge Hypergraphs: Extending Knowledge Graphs Beyond Binary Relations

Knowledge graphs store facts using relations between pairs of entities. ...
06/01/2019 ∙ by Bahare Fatemi, et al. ∙ 0 ∙ share

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Improved Knowledge Graph Embedding using Background Taxonomic Information

Knowledge graphs are used to represent relational information in terms o...
12/07/2018 ∙ by Bahare Fatemi, et al. ∙ 0 ∙ share

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Structure Learning for Relational Logistic Regression: An Ensemble Approach

We consider the problem of learning Relational Logistic Regression (RLR)...
08/06/2018 ∙ by Nandini Ramanan, et al. ∙ 14 ∙ share

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Record Linkage to Match Customer Names: A Probabilistic Approach

Consider the following problem: given a database of records indexed by n...
06/26/2018 ∙ by Bahare Fatemi, et al. ∙ 0 ∙ share

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SimplE Embedding for Link Prediction in Knowledge Graphs

The aim of knowledge graphs is to gather knowledge about the world and p...
02/13/2018 ∙ by Seyed Mehran Kazemi, et al. ∙ 0 ∙ share

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RelNN: A Deep Neural Model for Relational Learning

Statistical relational AI (StarAI) aims at reasoning and learning in noi...
12/07/2017 ∙ by Seyed Mehran Kazemi, et al. ∙ 0 ∙ share

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Comparing Aggregators for Relational Probabilistic Models

Relational probabilistic models have the challenge of aggregation, where...
07/25/2017 ∙ by Seyed Mehran Kazemi, et al. ∙ 0 ∙ share

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Domain Recursion for Lifted Inference with Existential Quantifiers

In recent work, we proved that the domain recursion inference rule makes...
07/24/2017 ∙ by Seyed Mehran Kazemi, et al. ∙ 0 ∙ share

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New Liftable Classes for First-Order Probabilistic Inference

Statistical relational models provide compact encodings of probabilistic...
10/26/2016 ∙ by Seyed Mehran Kazemi, et al. ∙ 0 ∙ share

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A Learning Algorithm for Relational Logistic Regression: Preliminary Results

Relational logistic regression (RLR) is a representation of conditional ...
06/28/2016 ∙ by Bahare Fatemi, et al. ∙ 0 ∙ share

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Why is Compiling Lifted Inference into a Low-Level Language so Effective?

First-order knowledge compilation techniques have proven efficient for l...
06/14/2016 ∙ by Seyed Mehran Kazemi, et al. ∙ 0 ∙ share

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Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (1994)

This is the Proceedings of the Tenth Conference on Uncertainty in Artifi...
04/13/2013 ∙ by Ramon Lopez de Mantaras, et al. ∙ 0 ∙ share

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Towards Completely Lifted Search-based Probabilistic Inference

The promise of lifted probabilistic inference is to carry out probabilis...
07/20/2011 ∙ by David Poole, et al. ∙ 0 ∙ share

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David Poole

Featured Co-authors

Kristian Kersting

Guy Van den Broeck

David Vazquez

Tushar Khot

Siamak Ravanbakhsh

Sriraam Natarajan

Fahiem Bacchus

Seyed Mehran Kazemi

Ramon Lopez de Mantaras

Angelika Kimmig

Gautam Kunapuli

Binarised Regression with Instance-Varying Costs: Evaluation using Impact Curves

A Probabilistic Approach for Predicting Landslides by Learning a Self-Aligned Deep Convolutional Model

Knowledge Hypergraphs: Extending Knowledge Graphs Beyond Binary Relations

Improved Knowledge Graph Embedding using Background Taxonomic Information

Structure Learning for Relational Logistic Regression: An Ensemble Approach

Record Linkage to Match Customer Names: A Probabilistic Approach

SimplE Embedding for Link Prediction in Knowledge Graphs

RelNN: A Deep Neural Model for Relational Learning

Comparing Aggregators for Relational Probabilistic Models

Domain Recursion for Lifted Inference with Existential Quantifiers

New Liftable Classes for First-Order Probabilistic Inference

A Learning Algorithm for Relational Logistic Regression: Preliminary Results

Why is Compiling Lifted Inference into a Low-Level Language so Effective?

Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (1994)

Towards Completely Lifted Search-based Probabilistic Inference