September, 2023
Fairness and Explainability in Machine Learning: A Formal Methods Approach
University of Michigan-Dearborn
August, 2022
Verifying Fairness in Machine Learning: A Formal Methods Approach
MPI-SWS (Max Planck Institute for Software Systems)
February, 2022
Algorithmic Fairness Verification with Graphical Models
AAAI 2022
January, 2022
Verification and Explanation of Fairness in Machine Learning
INRIA, Lille-Nord, France
February, 2021
Justicia: A Stochastic SAT Approach to Formally Verify Fairness
AAAI 2021
September, 2020
Classification Rules in Relaxed Logical Form
ECAI 2020
October, 2019
Incremental Approach to Interpretable Classification Rule Learning
CP 2019
August, 2019
The Flexible Socio Spatial Group Queries
VLDB 2019
August, 2019
Interpretable Classification Rules in Relaxed Logical Form
IJCAI 2019 workshop on DSO and XAI
January, 2019
IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules
AIES 2019