research
∙
07/12/2023
An OOD Multi-Task Perspective for Link Prediction with New Relation Types and Nodes
The task of inductive link prediction in (discrete) attributed multigrap...
share
research
∙
03/06/2023
MetaPhysiCa: OOD Robustness in Physics-informed Machine Learning
A fundamental challenge in physics-informed machine learning (PIML) is t...
research
∙
02/02/2023
Double Permutation Equivariance for Knowledge Graph Completion
This work provides a formalization of Knowledge Graphs (KGs) as a new cl...
research
∙
02/02/2023
Causal Lifting and Link Prediction
Current state-of-the-art causal models for link prediction assume an und...
research
∙
09/12/2022
Bias Challenges in Counterfactual Data Augmentation
Deep learning models tend not to be out-of-distribution robust primarily...
research
∙
08/26/2022
Veritas: Answering Causal Queries from Video Streaming Traces
In this paper, we seek to answer what-if questions - i.e., given recorde...
research
∙
05/30/2022
OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs
This work provides the first theoretical study on the ability of graph M...
research
∙
12/17/2021
Set Twister for Single-hop Node Classification
Node classification is a central task in relational learning, with the c...
research
∙
11/06/2021
Contextual Unsupervised Outlier Detection in Sequences
This work proposes an unsupervised learning framework for trajectory (se...
research
∙
10/01/2021
Reconstruction for Powerful Graph Representations
Graph neural networks (GNNs) have limited expressive power, failing to r...
research
∙
04/20/2021
Neural Networks for Learning Counterfactual G-Invariances from Single Environments
Despite – or maybe because of – their astonishing capacity to fit data, ...
research
∙
03/12/2021
On the Equivalence Between Temporal and Static Graph Representations for Observational Predictions
In this work we formalize the (pure observational) task of predicting no...
research
∙
03/08/2021
Size-Invariant Graph Representations for Graph Classification Extrapolations
In general, graph representation learning methods assume that the test a...
research
∙
12/07/2020
Sequential Stratified Regeneration: MCMC for Large State Spaces with an Application to Subgraph Counting Estimation
This work considers the general task of estimating the sum of a bounded ...
research
∙
10/08/2020
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models
Existing Graph Neural Network (GNN) methods that learn inductive unsuper...
research
∙
05/28/2020
Deceptive Deletions for Protecting Withdrawn Posts on Social Platforms
Over-sharing poorly-worded thoughts and personal information is prevalen...
research
∙
03/26/2020
A Collective Learning Framework to Boost GNN Expressiveness
Graph Neural Networks (GNNs) have recently been used for node and graph ...
research
∙
02/27/2020
Membership Inference Attacks and Defenses in Supervised Learning via Generalization Gap
This work studies membership inference (MI) attack against classifiers, ...
research
∙
02/11/2020
Infinity Learning: Learning Markov Chains from Aggregate Steady-State Observations
We consider the task of learning a parametric Continuous Time Markov Cha...
research
∙
10/01/2019
Deep Lifetime Clustering
The goal of lifetime clustering is to develop an inductive model that ma...
research
∙
10/01/2019
On the Equivalence between Node Embeddings and Structural Graph Representations
This work provides the first unifying theoretical framework for node emb...
research
∙
05/07/2019
Are Graph Neural Networks Miscalibrated?
Graph Neural Networks (GNNs) have proven to be successful in many classi...
research
∙
03/06/2019
Relational Pooling for Graph Representations
This work generalizes graph neural networks (GNNs) beyond those based on...
research
∙
11/05/2018
Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs
We consider a simple and overarching representation for permutation-inva...
research
∙
10/16/2018
Feedforward Neural Networks for Caching: Enough or Too Much?
We propose a caching policy that uses a feedforward neural network (FNN)...
research
∙
09/14/2018
Graph Pattern Mining and Learning through User-defined Relations (Extended Version)
In this work we propose R-GPM, a parallel computing framework for graph ...
research
∙
11/22/2017
From Monte Carlo to Las Vegas: Improving Restricted Boltzmann Machine Training Through Stopping Sets
We propose a Las Vegas transformation of Markov Chain Monte Carlo (MCMC)...
research
∙
09/20/2017
SBG-Sketch: A Self-Balanced Sketch for Labeled-Graph Stream Summarization
Applications in various domains rely on processing graph streams, e.g., ...
research
∙
07/24/2017
Stochastic Gradient Descent for Relational Logistic Regression via Partial Network Crawls
Research in statistical relational learning has produced a number of met...
research
∙
03/15/2017
Selective Harvesting over Networks
Active search (AS) on graphs focuses on collecting certain labeled nodes...
research
∙
11/03/2015
TribeFlow: Mining & Predicting User Trajectories
Which song will Smith listen to next? Which restaurant will Alice go to ...
research
∙
10/19/2015