95 citations · 271 across the 29 of their papers we have counts for
11 papers · 1 filter
Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information
Vishnu Dutt Sharma, John P. Dickerson, Pratap Tokekar
Green Security Games with real-time information (GSG-I) add the real-time information about the agents' movement to the typical GSG formulation. Prior works on GSG-I have used deep…
Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation
I. Elizabeth Kumar, Keegan E. Hines, John P. Dickerson
Credit is an essential component of financial wellbeing in America, and unequal access to it is a large factor in the economic disparities between demographic groups that exist tod…
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
Mucong Ding, Kezhi Kong, Jingling Li +4
Most state-of-the-art Graph Neural Networks (GNNs) can be defined as a form of graph convolution which can be realized by message passing between direct neighbors or beyond. To sca…
Pitfalls of Explainable ML: An Industry Perspective
Sahil Verma, Aditya Lahiri, John P. Dickerson +1
As machine learning (ML) systems take a more prominent and central role in contributing to life-impacting decisions, ensuring their trustworthiness and accountability is of utmost…
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints
Brian Brubach, Darshan Chakrabarti, John P. Dickerson +2
Metric clustering is fundamental in areas ranging from Combinatorial Optimization and Data Mining, to Machine Learning and Operations Research. However, in a variety of situations…
Technical Challenges for Training Fair Neural Networks
Valeriia Cherepanova, Vedant Nanda, Micah Goldblum +2
As machine learning algorithms have been widely deployed across applications, many concerns have been raised over the fairness of their predictions, especially in high stakes setti…