activity
20162022
most citedContextual Symmetries in Probabilistic Graphical Models

5 citations · 8 across the 9 of their papers we have counts for

collaborators
Showing cs.AIShow all

6 papers · 1 filter

cs.AI2020

Joint Spatio-Textual Reasoning for Answering Tourism Questions

Danish Contractor, Shashank Goel, Mausam +1

Our goal is to answer real-world tourism questions that seek Points-of-Interest (POI) recommendations. Such questions express various kinds of spatial and non-spatial constraints,…

cs.AI2018

Block-Value Symmetries in Probabilistic Graphical Models

Gagan Madan, Ankit Anand, Mausam +1

One popular way for lifted inference in probabilistic graphical models is to first merge symmetric states into a single cluster (orbit) and then use these for downstream inference,…

cs.AI2018

Lifted Marginal MAP Inference

Vishal Sharma, Noman Ahmed Sheikh, Happy Mittal +2

Lifted inference reduces the complexity of inference in relational probabilistic models by identifying groups of constants (or atoms) which behave symmetric to each other. A number…

cs.AI2017

Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical Models

Ankit Anand, Ritesh Noothigattu, Parag Singla +1

Lifted inference algorithms commonly exploit symmetries in a probabilistic graphical model (PGM) for efficient inference. However, existing algorithms for Boolean-valued domains ca…

cs.AI2016

Lifted Region-Based Belief Propagation

David Smith, Parag Singla, Vibhav Gogate

Due to the intractable nature of exact lifted inference, research has recently focused on the discovery of accurate and efficient approximate inference algorithms in Statistical Re…

cs.AI20165 cited

Contextual Symmetries in Probabilistic Graphical Models

Ankit Anand, Aditya Grover, Mausam +1

An important approach for efficient inference in probabilistic graphical models exploits symmetries among objects in the domain. Symmetric variables (states) are collapsed into met…