StARLinG Lab

BoostSRL

BoostSRL: "Boosting for Statistical Relational Learning." A gradient-boosting based approach for learning different types of SRL models.

BoostSRL-Lite

This repository contains code base for the slim version of BoostSRL. Performance wise, they are the same, but differs in the volume of redundant code removed in this slim version

DeepRePReL

KiGB

Knowledge-intensive Gradient Boosting: A unified framework for learning gradient boosted decision trees for regression and classification tasks while leveraging human advice for achieving better performance.

Relational-Boosted-Bandits

Code repository for the work Relational Boosted Bandits, AAAI'21

RePReL

An implementation of the paper Kokel et al. ICAPS 2021, RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction.

RRBM-Tensorflow

TensorFlow implementation of "Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach"

starling.utdallas.edu

Development repository for the STARLinG Lab's webpage. Built wtih Jekyll, jQuery, and the minimal-mistakes Jekyll theme.

Textual_Annotation_Interface

The interface lets experts annotate textual data to help a model

TPM-Workshop

starling-lab/BoostSRL

BoostSRL: "Boosting for Statistical Relational Learning." A gradient-boosting based approach for learning different types of SRL models.

starling-lab/starling.utdallas.edu

Development repository for the STARLinG Lab's webpage. Built wtih Jekyll, jQuery, and the minimal-mistakes Jekyll theme.

starling-lab/DeepRePReL

starling-lab/RRBM-Tensorflow

TensorFlow implementation of "Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach"

starling-lab/KiGB

Knowledge-intensive Gradient Boosting: A unified framework for learning gradient boosted decision trees for regression and classification tasks while leveraging human advice for achieving better performance.

starling-lab/BoostSRL-Lite

This repository contains code base for the slim version of BoostSRL. Performance wise, they are the same, but differs in the volume of redundant code removed in this slim version

starling-lab/Relational-Boosted-Bandits

Code repository for the work Relational Boosted Bandits, AAAI'21

starling-lab/RePReL

An implementation of the paper Kokel et al. ICAPS 2021, RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction.

starling-lab/TPM-Workshop

starling-lab/JA-Walk-ER

starling-lab/MLNBoostDB

Learning Boosted MLN with in-memory Relational Database integration (Malec et al. ILP 2016). This is an extension where wrapper ensures same command line argument structure as MLN-Boost. Most arguments are same as the original MLN-Boost(Khot et al. ICDM 2011) platform. Few that are different have been stated below.

starling-lab/Textual_Annotation_Interface

The interface lets experts annotate textual data to help a model

starling-lab/ExSPN-SPFlow

ExSPN: Explaining Sum-Product Networks

starling-lab/GOCI

Guided One-shot Concept Induction

starling-lab/KIL-CN

Knowledge-Intensive learning of Cutset Networks

starling-lab/PI_GBM