37 citations · 88 across the 9 of their papers we have counts for
16 papers
Anticipating Technical Expertise and Capability Evolution in Research Communities using Dynamic Graph Transformers
Sameera Horawalavithana, Ellyn Ayton, Anastasiya Usenko +2
The ability to anticipate technical expertise and capability evolution trends globally is essential for national and global security, especially in safety-critical domains like nuc…
EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs
Sameera Horawalavithana, Ellyn Ayton, Anastasiya Usenko +5
Machine learning models that learn from dynamic graphs face nontrivial challenges in learning and inference as both nodes and edges change over time. The existing large-scale graph…
Identifying Causal Influences on Publication Trends and Behavior: A Case Study of the Computational Linguistics Community
Maria Glenski, Svitlana Volkova
Drawing causal conclusions from observational real-world data is a very much desired but challenging task. In this paper we present mixed-method analyses to investigate causal infl…
Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages
Maria Glenski, Ellyn Ayton, Robin Cosbey +2
Evaluating model robustness is critical when developing trustworthy models not only to gain deeper understanding of model behavior, strengths, and weaknesses, but also to develop f…
Evaluating Deception Detection Model Robustness To Linguistic Variation
Maria Glenski, Ellyn Ayton, Robin Cosbey +2
With the increasing use of machine-learning driven algorithmic judgements, it is critical to develop models that are robust to evolving or manipulated inputs. We propose an extensi…
Evaluating Neural Machine Comprehension Model Robustness to Noisy Inputs and Adversarial Attacks
Winston Wu, Dustin Arendt, Svitlana Volkova
We evaluate machine comprehension models' robustness to noise and adversarial attacks by performing novel perturbations at the character, word, and sentence level. We experiment wi…