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Related publications (266)

Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations

Text-based games (TBGs) have emerged as useful benchmarks for evaluating progress at the intersection of grounded language understanding and reinforcement learning (RL). Recent work has proposed the use of external knowledge to improve the efficiency of RL ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

Partition and Code: learning how to compress graphs

Michael Bronstein, Andreas Loukas, Nikolaos Karalias, Georgios Bouritsas

Can we use machine learning to compress graph data? The absence of ordering in graphs poses a significant challenge to conventional compression algorithms, limiting their attainable gains as well as their ability to discover relevant patterns. On the other ...

2021

Tree in Tree: from Decision trees to Decision Graphs

Mahsa Shoaran, Bingzhao Zhu

Decision trees have been widely used as classifiers in many machine learning applications thanks to their lightweight and interpretable decision process. This paper introduces Tree in Tree decision graph (TnT), a framework that extends the conventional dec ...

Curran Associates, Inc, (NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing Systems)2021

node2coords: Graph Representation Learning with Wasserstein Barycenters

Pascal Frossard, Effrosyni Simou

In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods learn representations that cannot be interpreted in a straightforward way and that are relat ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Adaptive majority problems for restricted query graphs and for weighted sets

Abhishek Methuku, Balázs Keszegh

Suppose that the vertices of a graph G are colored with two colors in an unknown way. The color that occurs on more than half of the vertices is called the majority color (if it exists), and any vertex of this color is called a majority vertex. We study th ...

ELSEVIER2021

Representing graphs through data with learning and optimal transport

Hermina Petric Maretic

Graphs offer a simple yet meaningful representation of relationships between data. Thisrepresentation is often used in machine learning algorithms in order to incorporate structuralor geometric information about data. However, it can also be used in an inv ...

EPFL2021

How Florist Apprentices Explore Bouquet Designs: Supporting Design Space Exploration for Vocational Students

Pierre Dillenbourg, Kevin Gonyop Kim, Catharine Regina Monika Maria Oertel Genannt Bierbach

Context: Exploring the design space is an important process in a design task. In this study, we considered design space exploration for the learners in vocational education and training (VET). The goal of the study was to investigate how they explore the d ...

2021

Scalable Robust Graph Embedding with Spark

Karl Aberer, Quoc Viet Hung Nguyen, Chi Thang Duong, Trung-Dung Hoang

Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to larg ...

ASSOC COMPUTING MACHINERY2021

Representation Learning for Multi-relational Data

Eda Bayram

Recent years have witnessed a rise in real-world data captured with rich structural information that can be better depicted by multi-relational or heterogeneous graphs.However, research on relational representation learning has so far mostly focused on the ...

EPFL2021

Metric dimension of critical Galton–Watson trees and linear preferential attachment trees

Gergely Odor

The metric dimension of a graph G is the minimal size of a subset R of vertices of G that, upon reporting their graph distance from a distinguished (source) vertex v⋆, enable unique identification of the source vertex v⋆ among all possible vertices of G. I ...

2021

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