102 citations · 256 across the 62 of their papers we have counts for
8 papers · 1 filter
Fairness Degrading Adversarial Attacks Against Clustering Algorithms
Anshuman Chhabra, Adish Singla, Prasant Mohapatra
Clustering algorithms are ubiquitous in modern data science pipelines, and are utilized in numerous fields ranging from biology to facility location. Due to their widespread use, e…
Reinforcement Learning Under Algorithmic Triage
Eleni Straitouri, Adish Singla, Vahid Balazadeh Meresht +1
Methods to learn under algorithmic triage have predominantly focused on supervised learning settings where each decision, or prediction, is independent of each other. Under algorit…
Reinforcement Learning for Education: Opportunities and Challenges
Adish Singla, Anna N. Rafferty, Goran Radanovic +1
This survey article has grown out of the RL4ED workshop organized by the authors at the Educational Data Mining (EDM) 2021 conference. We organized this workshop as part of a commu…
Fair Clustering Using Antidote Data
Anshuman Chhabra, Adish Singla, Prasant Mohapatra
Clustering algorithms are widely utilized for many modern data science applications. This motivates the need to make outputs of clustering algorithms fair. Traditionally, new fair…
Loss-Aversively Fair Classification
Junaid Ali, Muhammad Bilal Zafar, Adish Singla +1
The use of algorithmic (learning-based) decision making in scenarios that affect human lives has motivated a number of recent studies to investigate such decision making systems fo…
Accounting for Model Uncertainty in Algorithmic Discrimination
Junaid Ali, Preethi Lahoti, Krishna P. Gummadi
Traditional approaches to ensure group fairness in algorithmic decision making aim to equalize ``total'' error rates for different subgroups in the population. In contrast, we argu…