b'Bruno Ribeiro'

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Featured Co-authors

Don Towsley
73 publications
Ananthram Swami
52 publications
Gitta Kutyniok
47 publications
Christos Faloutsos
38 publications
Jiacheng Li
29 publications
Konstantin Avrachenkov
26 publications
Giovanni Neglia
22 publications
Jennifer Neville
22 publications
Christopher Morris
21 publications
Ninghui Li
21 publications
Vinayak Rao
16 publications

research

∙ 03/06/2023

MetaPhysiCa: OOD Robustness in Physics-informed Machine Learning

A fundamental challenge in physics-informed machine learning (PIML) is t...

2 S Chandra Mouli, et al. ∙

research

∙ 02/02/2023

Double Permutation Equivariance for Knowledge Graph Completion

This work provides a formalization of Knowledge Graphs (KGs) as a new cl...

30 Jianfei Gao, et al. ∙

research

∙ 02/02/2023

Causal Lifting and Link Prediction

Current state-of-the-art causal models for link prediction assume an und...

1 Leonardo Cotta, et al. ∙

research

∙ 09/12/2022

Bias Challenges in Counterfactual Data Augmentation

Deep learning models tend not to be out-of-distribution robust primarily...

38 S Chandra Mouli, et al. ∙

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...

0 Chandan Bothra, et al. ∙

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...

0 Yangze Zhou, et al. ∙

research

∙ 12/17/2021

Set Twister for Single-hop Node Classification

Node classification is a central task in relational learning, with the c...

0 Yangze Zhou, et al. ∙

research

∙ 11/06/2021

Contextual Unsupervised Outlier Detection in Sequences

This work proposes an unsupervised learning framework for trajectory (se...

0 Mohamed A. Zahran, et al. ∙

research

∙ 10/01/2021

Reconstruction for Powerful Graph Representations

Graph neural networks (GNNs) have limited expressive power, failing to r...

12 Leonardo Cotta, et al. ∙

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, ...

0 S Chandra Mouli, et al. ∙

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...

0 Jianfei Gao, et al. ∙

research

∙ 03/08/2021

Size-Invariant Graph Representations for Graph Classification Extrapolations

In general, graph representation learning methods assume that the test a...

0 Beatrice Bevilacqua, et al. ∙

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 ...

0 Carlos H. C. Teixeira, et al. ∙

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...

0 Leonardo Cotta, et al. ∙

research

∙ 05/28/2020

Deceptive Deletions for Protecting Withdrawn Posts on Social Platforms

Over-sharing poorly-worded thoughts and personal information is prevalen...

0 Mohsen Minaei, et al. ∙

research

∙ 03/26/2020

A Collective Learning Framework to Boost GNN Expressiveness

Graph Neural Networks (GNNs) have recently been used for node and graph ...

0 Mengyue Hang, et al. ∙

research

∙ 02/27/2020

Membership Inference Attacks and Defenses in Supervised Learning via Generalization Gap

This work studies membership inference (MI) attack against classifiers, ...

0 Jiacheng Li, et al. ∙

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...

0 Jianfei Gao, et al. ∙

research

∙ 10/01/2019

Deep Lifetime Clustering

The goal of lifetime clustering is to develop an inductive model that ma...

0 S Chandra Mouli, et al. ∙

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...

0 Balasubramaniam Srinivasan, et al. ∙

research

∙ 05/07/2019

Are Graph Neural Networks Miscalibrated?

Graph Neural Networks (GNNs) have proven to be successful in many classi...

0 Leonardo Teixeira, et al. ∙

research

∙ 03/06/2019

Relational Pooling for Graph Representations

This work generalizes graph neural networks (GNNs) beyond those based on...

0 Ryan L. Murphy, et al. ∙

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...

0 Ryan L. Murphy, et al. ∙

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)...

0 Vladyslav Fedchenko, et al. ∙

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 ...

0 Carlos H. C. Teixeira, et al. ∙

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)...

0 Pedro H. P. Savarese, et al. ∙

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., ...

0 Mohamed S. Hassan, et al. ∙

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...

0 Jiasen Yang, et al. ∙

research

∙ 03/15/2017

Selective Harvesting over Networks

Active search (AS) on graphs focuses on collecting certain labeled nodes...

0 Fabricio Murai, et al. ∙

research

∙ 11/03/2015

TribeFlow: Mining & Predicting User Trajectories

Which song will Smith listen to next? Which restaurant will Alice go to ...

0 Flavio Figueiredo, et al. ∙

research

∙ 10/19/2015

Bayesian Inference of Online Social Network Statistics via Lightweight Random Walk Crawls

Online social networks (OSN) contain extensive amount of information abo...

0 Konstantin Avrachenkov, et al. ∙