67 citations · 350 across the 31 of their papers we have counts for
32 papers
Replacing Paths with Connection-Biased Attention for Knowledge Graph Completion
Sharmishtha Dutta, Alex Gittens, Mohammed J. Zaki +1
Knowledge graph (KG) completion aims to identify additional facts that can be inferred from the existing facts in the KG. Recent developments in this field have explored this task…
Iterative thresholding for non-linear learning in the strong -contamination model
Arvind Rathnashyam, Alex Gittens
We derive approximation bounds for learning single neuron models using thresholded gradient descent when both the labels and the covariates are possibly corrupted adversarially. We…
Exploiting Missing Data Remediation Strategies using Adversarial Missingness Attacks
Deniz Koyuncu, Alex Gittens, Bülent Yener +1
Adversarial Missingness (AM) attacks aim to manipulate model fitting by carefully engineering a missing data problem to achieve a specific malicious objective. AM attacks are signi…
Improving Neural Ranking Models with Traditional IR Methods
Anik Saha, Oktie Hassanzadeh, Alex Gittens +3
Neural ranking methods based on large transformer models have recently gained significant attention in the information retrieval community, and have been adopted by major commercia…
A Cross-Domain Evaluation of Approaches for Causal Knowledge Extraction
Anik Saha, Oktie Hassanzadeh, Alex Gittens +3
Causal knowledge extraction is the task of extracting relevant causes and effects from text by detecting the causal relation. Although this task is important for language understan…
Deception by Omission: Using Adversarial Missingness to Poison Causal Structure Learning
Deniz Koyuncu, Alex Gittens, Bülent Yener +1
Inference of causal structures from observational data is a key component of causal machine learning; in practice, this data may be incompletely observed. Prior work has demonstrat…