56 citations · 175 across the 29 of their papers we have counts for
30 papers
Grounding Computer Use Agents on Human Demonstrations
Aarash Feizi, Shravan Nayak, Xiangru Jian +14
Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen elements. While large datasets exist for web…
Weak Supervision for Real World Graphs
Pratheeksha Nair, Reihaneh Rabbany
Node classification in real world graphs often suffers from label scarcity and noise, especially in high stakes domains like human trafficking detection and misinformation monitori…
PairBench: Are Vision-Language Models Reliable at Comparing What They See?
Aarash Feizi, Sai Rajeswar, Adriana Romero-Soriano +4
Understanding how effectively large vision language models (VLMs) compare visual inputs is crucial across numerous applications, yet this fundamental capability remains insufficien…
Temporal Graph Analysis with TGX
Razieh Shirzadkhani, Shenyang Huang, Elahe Kooshafar +2
Real-world networks, with their evolving relations, are best captured as temporal graphs. However, existing software libraries are largely designed for static graphs where the dyna…
Combining Confidence Elicitation and Sample-based Methods for Uncertainty Quantification in Misinformation Mitigation
Mauricio Rivera, Jean-François Godbout, Reihaneh Rabbany +1
Large Language Models have emerged as prime candidates to tackle misinformation mitigation. However, existing approaches struggle with hallucinations and overconfident predictions.…
Comparing GPT-4 and Open-Source Language Models in Misinformation Mitigation
Tyler Vergho, Jean-Francois Godbout, Reihaneh Rabbany +1
Recent large language models (LLMs) have been shown to be effective for misinformation detection. However, the choice of LLMs for experiments varies widely, leading to uncertain co…
GPS-SSL: Guided Positive Sampling to Inject Prior Into Self-Supervised Learning
Aarash Feizi, Randall Balestriero, Adriana Romero-Soriano +1
We propose Guided Positive Sampling Self-Supervised Learning (GPS-SSL), a general method to inject a priori knowledge into Self-Supervised Learning (SSL) positive samples selection…
Uncertainty Resolution in Misinformation Detection
Yury Orlovskiy, Camille Thibault, Anne Imouza +3
Misinformation poses a variety of risks, such as undermining public trust and distorting factual discourse. Large Language Models (LLMs) like GPT-4 have been shown effective in mit…
Towards Detecting Contextual Real-Time Toxicity for In-Game Chat
Zachary Yang, Nicolas Grenan-Godbout, Reihaneh Rabbany
Real-time toxicity detection in online environments poses a significant challenge, due to the increasing prevalence of social media and gaming platforms. We introduce ToxBuster, a…
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Dominique Beaini, Shenyang Huang, Joao Alex Cunha +32
Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, where datasets are often hand-curated, and…
Party Prediction for Twitter
Kellin Pelrine, Anne Imouza, Zachary Yang +8
A large number of studies on social media compare the behaviour of users from different political parties. As a basic step, they employ a predictive model for inferring their polit…
Open, Closed, or Small Language Models for Text Classification?
Hao Yu, Zachary Yang, Kellin Pelrine +2
Recent advancements in large language models have demonstrated remarkable capabilities across various NLP tasks. But many questions remain, including whether open-source models mat…
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
Shenyang Huang, Farimah Poursafaei, Jacob Danovitch +7
We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning mod…
ToxBuster: In-game Chat Toxicity Buster with BERT
Zachary Yang, Yasmine Maricar, MohammadReza Davari +2
Detecting toxicity in online spaces is challenging and an ever more pressing problem given the increase in social media and gaming consumption. We introduce ToxBuster, a simple and…
Fast and Attributed Change Detection on Dynamic Graphs with Density of States
Shenyang Huang, Jacob Danovitch, Guillaume Rabusseau +1
How can we detect traffic disturbances from international flight transportation logs or changes to collaboration dynamics in academic networks? These problems can be formulated as…
Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4
Kellin Pelrine, Anne Imouza, Camille Thibault +5
Misinformation poses a critical societal challenge, and current approaches have yet to produce an effective solution. We propose focusing on generalization, uncertainty, and how to…
Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs
Shenyang Huang, Samy Coulombe, Yasmeen Hitti +2
Dynamic graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly detection in temporal graphs is crucial for…
Active Keyword Selection to Track Evolving Topics on Twitter
Sacha Lévy, Farimah Poursafaei, Kellin Pelrine +1
How can we study social interactions on evolving topics at a mass scale? Over the past decade, researchers from diverse fields such as economics, political science, and public heal…
Revisiting Hotels-50K and Hotel-ID
Aarash Feizi, Arantxa Casanova, Adriana Romero-Soriano +1
In this paper, we propose revisited versions for two recent hotel recognition datasets: Hotels50K and Hotel-ID. The revisited versions provide evaluation setups with different leve…
Towards Better Evaluation for Dynamic Link Prediction
Farimah Poursafaei, Shenyang Huang, Kellin Pelrine +1
Despite the prevalence of recent success in learning from static graphs, learning from time-evolving graphs remains an open challenge. In this work, we design new, more stringent e…
Graph Attention Networks with Positional Embeddings
Liheng Ma, Reihaneh Rabbany, Adriana Romero-Soriano
Graph Neural Networks (GNNs) are deep learning methods which provide the current state of the art performance in node classification tasks. GNNs often assume homophily -- neighbori…
The Surprising Performance of Simple Baselines for Misinformation Detection
Kellin Pelrine, Jacob Danovitch, Reihaneh Rabbany
As social media becomes increasingly prominent in our day to day lives, it is increasingly important to detect informative content and prevent the spread of disinformation and unve…
Contact Graph Epidemic Modelling of COVID-19 for Transmission and Intervention Strategies
Abby Leung, Xiaoye Ding, Shenyang Huang +1
The coronavirus disease 2019 (COVID-19) pandemic has quickly become a global public health crisis unseen in recent years. It is known that the structure of the human contact networ…
Incorporating Dynamic Flight Network in SEIR to Model Mobility between Populations
Xiaoye Ding, Shenyang Huang, Abby Leung +1
Current efforts of modelling COVID-19 are often based on the standard compartmental models such as SEIR and their variations. As pre-symptomatic and asymptomatic cases can spread t…