286 citations · 534 across the 36 of their papers we have counts for
5 papers · 1 filter
CONFAIR: Configurable and Interpretable Algorithmic Fairness
Ankit Kulshrestha, Ilya Safro
The rapid growth of data in the recent years has led to the development of complex learning algorithms that are often used to make decisions in real world. While the positive impac…
Transferability of optimal QAOA parameters between random graphs
Alexey Galda, Xiaoyuan Liu, Danylo Lykov +2
The Quantum approximate optimization algorithm (QAOA) is one of the most promising candidates for achieving quantum advantage through quantum-enhanced combinatorial optimization. I…
Coping with Mistreatment in Fair Algorithms
Ankit Kulshrestha, Ilya Safro
Machine learning actively impacts our everyday life in almost all endeavors and domains such as healthcare, finance, and energy. As our dependence on the machine learning increases…
Accelerating COVID-19 research with graph mining and transformer-based learning
Ilya Tyagin, Ankit Kulshrestha, Justin Sybrandt +3
In 2020, the White House released the, "Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset," wherein artificial intelligence experts are asked to collect…
Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers
Xiaoyuan Liu, Anthony Angone, Ruslan Shaydulin +3
Combinatorial optimization on near-term quantum devices is a promising path to demonstrating quantum advantage. However, the capabilities of these devices are constrained by high n…