44 citations · 59 across the 5 of their papers we have counts for
15 papers
Certifying Robustness to Programmable Data Bias in Decision Trees
Anna P. Meyer, Aws Albarghouthi, Loris D'Antoni
Datasets can be biased due to societal inequities, human biases, under-representation of minorities, etc. Our goal is to certify that models produced by a learning algorithm are po…
Introduction to Neural Network Verification
Aws Albarghouthi
Deep learning has transformed the way we think of software and what it can do. But deep neural networks are fragile and their behaviors are often surprising. In many settings, we n…
Certified Robustness to Programmable Transformations in LSTMs
Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni
Deep neural networks for natural language processing are fragile in the face of adversarial examples -- small input perturbations, like synonym substitution or word duplication, wh…
Learning Differentially Private Mechanisms
Subhajit Roy, Justin Hsu, Aws Albarghouthi
Differential privacy is a formal, mathematical definition of data privacy that has gained traction in academia, industry, and government. The task of correctly constructing differe…
A Comparative Exploration of ML Techniques for Tuning Query Degree of Parallelism
Zhiwei Fan, Rathijit Sen, Paraschos Koutris +1
There is a large body of recent work applying machine learning (ML) techniques to query optimization and query performance prediction in relational database management systems (RDB…
Robustness to Programmable String Transformations via Augmented Abstract Training
Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni
Deep neural networks for natural language processing tasks are vulnerable to adversarial input perturbations. In this paper, we present a versatile language for programmatically sp…