148 citations · 160 across the 17 of their papers we have counts for
19 papers
Knowledge-based Refinement of Scientific Publication Knowledge Graphs
Siwen Yan, Phillip Odom, Sriraam Natarajan
We consider the problem of identifying authorship by posing it as a knowledge graph construction and refinement. To this effect, we model this problem as learning a probabilistic l…
MACOptions: Multi-Agent Learning with Centralized Controller and Options Framework
Alakh Aggarwal, Rishita Bansal, Parth Padalkar +1
These days automation is being applied everywhere. In every environment, planning for the actions to be taken by the agents is an important aspect. In this paper, we plan to implem…
Human-guided Collaborative Problem Solving: A Natural Language based Framework
Harsha Kokel, Mayukh Das, Rakibul Islam +10
We consider the problem of human-machine collaborative problem solving as a planning task coupled with natural language communication. Our framework consists of three components --…
Explainable Models via Compression of Tree Ensembles
Siwen Yan, Sriraam Natarajan, Saket Joshi +2
Ensemble models (bagging and gradient-boosting) of relational decision trees have proved to be one of the most effective learning methods in the area of probabilistic logic models…
Relational Neural Markov Random Fields
Yuqiao Chen, Sriraam Natarajan, Nicholas Ruozzi
Statistical Relational Learning (SRL) models have attracted significant attention due to their ability to model complex data while handling uncertainty. However, most of these mode…
Dynamic probabilistic logic models for effective abstractions in RL
Harsha Kokel, Arjun Manoharan, Sriraam Natarajan +2
State abstraction enables sample-efficient learning and better task transfer in complex reinforcement learning environments. Recently, we proposed RePReL (Kokel et al. 2021), a hie…
Explaining Deep Tractable Probabilistic Models: The sum-product network case
Athresh Karanam, Saurabh Mathur, Predrag Radivojac +3
We consider the problem of explaining a class of tractable deep probabilistic models, the Sum-Product Networks (SPNs) and present an algorithm ExSPN to generate explanations. To th…
A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children
Michael A. Skinner, Lakshmi Raman, Neel Shah +2
The increased use of electronic health records has made possible the automated extraction of medical policies from patient records to aid in the development of clinical decision su…
Non-Parametric Learning of Lifted Restricted Boltzmann Machines
Navdeep Kaur, Gautam Kunapuli, Sriraam Natarajan
We consider the problem of discriminatively learning restricted Boltzmann machines in the presence of relational data. Unlike previous approaches that employ a rule learner (for st…
User Friendly Automatic Construction of Background Knowledge: Mode Construction from ER Diagrams
Alexander L. Hayes, Mayukh Das, Phillip Odom +1
One of the key advantages of Inductive Logic Programming systems is the ability of the domain experts to provide background knowledge as modes that allow for efficient search throu…
Neural Networks for Relational Data
Navdeep Kaur, Gautam Kunapuli, Saket Joshi +2
While deep networks have been enormously successful over the last decade, they rely on flat-feature vector representations, which makes them unsuitable for richly structured domain…
Knowledge-augmented Column Networks: Guiding Deep Learning with Advice
Mayukh Das, Devendra Singh Dhami, Yang Yu +2
Recently, deep models have had considerable success in several tasks, especially with low-level representations. However, effective learning from sparse noisy samples is a major ch…
Human-Guided Learning of Column Networks: Augmenting Deep Learning with Advice
Mayukh Das, Yang Yu, Devendra Singh Dhami +2
Recently, deep models have been successfully applied in several applications, especially with low-level representations. However, sparse, noisy samples and structured domains (with…
Structure Learning for Relational Logistic Regression: An Ensemble Approach
Nandini Ramanan, Gautam Kunapuli, Tushar Khot +5
We consider the problem of learning Relational Logistic Regression (RLR). Unlike standard logistic regression, the features of RLRs are first-order formulae with associated weight…
Preference-Guided Planning: An Active Elicitation Approach
Mayukh Das, Phillip Odom, Md. Rakibul Islam +4
Planning with preferences has been employed extensively to quickly generate high-quality plans. However, it may be difficult for the human expert to supply this information without…
Sum-Product Networks for Hybrid Domains
Alejandro Molina, Antonio Vergari, Nicola Di Mauro +3
While all kinds of mixed data -from personal data, over panel and scientific data, to public and commercial data- are collected and stored, building probabilistic graphical models…
Application of Statistical Relational Learning to Hybrid Recommendation Systems
Shuo Yang, Mohammed Korayem, Khalifeh AlJadda +2
Recommendation systems usually involve exploiting the relations among known features and content that describe items (content-based filtering) or the overlap of similar users who i…
Learning Relational Dependency Networks for Relation Extraction
Dileep Viswanathan, Ameet Soni, Jude Shavlik +1
We consider the task of KBP slot filling -- extracting relation information from newswire documents for knowledge base construction. We present our pipeline, which employs Relation…
Counting Belief Propagation
Kristian Kersting, Babak Ahmadi, Sriraam Natarajan
A major benefit of graphical models is that most knowledge is captured in the model structure. Many models, however, produce inference problems with a lot of symmetries not reflect…