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Found 39 papers, 9 papers with code
Combining Planning and Reinforcement Learning for Solving Relational Multiagent Domains
Multiagent Reinforcement Learning (MARL) poses significant challenges due to the exponential growth of state and action spaces and the non-stationary nature of multiagent environments.
A Unified Framework for Human-Allied Learning of Probabilistic Circuits
Probabilistic Circuits (PCs) have emerged as an efficient framework for representing and learning complex probability distributions.
Credibility-Aware Multi-Modal Fusion Using Probabilistic Circuits
We consider the problem of late multi-modal fusion for discriminative learning.
Building Expressive and Tractable Probabilistic Generative Models: A Review
We present a comprehensive survey of the advancements and techniques in the field of tractable probabilistic generative modeling, primarily focusing on Probabilistic Circuits (PCs).
Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposals
Building teams and promoting collaboration are two very common business activities.
Knowledge-based Refinement of Scientific Publication Knowledge Graphs
We consider the problem of identifying authorship by posing it as a knowledge graph construction and refinement.
MACOptions: Multi-Agent Learning with Centralized Controller and Options Framework
These days automation is being applied everywhere.
Human-guided Collaborative Problem Solving: A Natural Language based Framework
We consider the problem of human-machine collaborative problem solving as a planning task coupled with natural language communication.
Explainable Models via Compression of Tree Ensembles
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 (PLMs).
Explaining Deep Tractable Probabilistic Models: The sum-product network case
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.
Relational Neural Markov Random Fields
Statistical Relational Learning (SRL) models have attracted significant attention due to their ability to model complex data while handling uncertainty.
Dynamic probabilistic logic models for effective abstractions in RL
State abstraction enables sample-efficient learning and better task transfer in complex reinforcement learning environments.
Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach
Predicting and discovering drug-drug interactions (DDIs) using machine learning has been studied extensively.
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models
While probabilistic models are an important tool for studying causality, doing so suffers from the intractability of inference.
A Statistical Relational Approach to Learning Distance-based GCNs
We consider the problem of learning distance-based Graph Convolutional Networks (GCNs) for relational data.
Relational Boosted Bandits
Contextual bandits algorithms have become essential in real-world user interaction problems in recent years.
The Curious Case of Stacking Boosted Relational Dependency Networks
Reducing bias while learning and inference is an important requirement to achieve generalizable and better performing models.
Fitted Q-Learning for Relational Domains
We consider the problem of Approximate Dynamic Programming in relational domains.
Knowledge Graph Alignment using String Edit Distance
In this work, we propose a novel knowledge graph alignment technique based upon string edit distance that exploits the type information between entities and can find similarity between relations of any arity
A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children
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 support systems.
Non-Parametric Learning of Lifted Restricted Boltzmann Machines
We consider the problem of discriminatively learning restricted Boltzmann machines in the presence of relational data.
Lifted Hybrid Variational Inference
We demonstrate that the proposed variational methods are both scalable and can take advantage of approximate model symmetries, even in the presence of a large amount of continuous evidence.
Non-Parametric Learning of Gaifman Models
We consider the problem of structure learning for Gaifman models and learn relational features that can be used to derive feature representations from a knowledge base.
User Friendly Automatic Construction of Background Knowledge: Mode Construction from ER Diagrams
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 through the space of hypotheses.
One-Shot Induction of Generalized Logical Concepts via Human Guidance
First, we define a distance measure between candidate concept representations that improves the efficiency of search for target concept and generalization.
Beyond Textual Data: Predicting Drug-Drug Interactions from Molecular Structure Images using Siamese Neural Networks
Predicting and discovering drug-drug interactions (DDIs) is an important problem and has been studied extensively both from medical and machine learning point of view.
Neural Networks for Relational Data
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 domains such as those arising in applications like social network analysis.
Knowledge-augmented Column Networks: Guiding Deep Learning with Advice
Recently, deep models have had considerable success in several tasks, especially with low-level representations.
Human-Guided Column Networks: Augmenting Deep Learning with Advice
While extremely successful in several applications, especially with low-level representations; sparse, noisy samples and structured domains (with multiple objects and interactions) are some of the open challenges in most deep models.
Human-Guided Learning of Column Networks: Augmenting Deep Learning with Advice
Recently, deep models have been successfully applied in several applications, especially with low-level representations.
GLAD: GLocalized Anomaly Detection via Human-in-the-Loop Learning
Human analysts that use anomaly detection systems in practice want to retain the use of simple and explainable global anomaly detectors.
Structure Learning for Relational Logistic Regression: An Ensemble Approach
We consider the problem of learning Relational Logistic Regression (RLR).
Preference-Guided Planning: An Active Elicitation Approach
Planning with preferences has been employed extensively to quickly generate high-quality plans.
Sum-Product Networks for Hybrid Domains
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 for these hybrid domains becomes more difficult.
Towards Problem Solving Agents that Communicate and Learn
Agents that communicate back and forth with humans to help them execute non-linguistic tasks are a long sought goal of AI.
Application of Statistical Relational Learning to Hybrid Recommendation Systems
In this paper, we proposed a way to adapt the state-of-the-art in SRL learning approaches to construct a real hybrid recommendation system.
Learning Relational Dependency Networks for Relation Extraction
We consider the task of KBP slot filling -- extracting relation information from newswire documents for knowledge base construction.
Multiplicative Forests for Continuous-Time Processes
Learning temporal dependencies between variables over continuous time is an important and challenging task.
