Publications
- J. Finocchiaro, R. Maio, F. Monachou, G. K. Patro, M. Raghavan, A.-A. Stoica, S. Tsirtsis. Bridging Machine Learning and Mechanism Design Towards Algorithmic Fairness. Accepted to FAccT ‘21. (preprint)
- R. Maio and A. Chaintreau. Incentives Needed for Low-Cost Fair Lateral Data Reuse. FODS ‘20. (publisher)
- S. Cleary and R. Maio. Estimating The Number of Difficult Tree Pairs with Respect to the Rotation Distance Problem. Journal of Combinatorial Mathematics and Combinatorial Computing. (preprint)
- S. Cleary and R. Maio. Edge Conflicts Do Not Determine Geodesics in the Associahedron. SIAM Journal on Discrete Mathematics. (publisher)
Preprints
- S. Cleary and R. Maio. An Efficient Sampling Algorithm for Difficult Tree Pairs. arXiv preprint arXiv:2001.06422
ACM Digital Library: Author-izer Service Snippets and Links
You can download a copy of these papers free-of-charge from the ACM Digital Library by following the links.
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research-article
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness
- Jessie Finocchiaro,
- Roland Maio,
- Faidra Monachou,
- Gourab K Patro,
- Manish Raghavan,
- Ana-Andreea Stoica,
- Stratis Tsirtsis
Decision-making systems increasingly orchestrate our world: how to intervene on the algorithmic components to build fair and equitable systems is therefore a question of utmost importance; one that is substantially complicated by the context-dependent ...
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research-article
Incentives Needed for Low-Cost Fair Lateral Data Reuse
A central goal of algorithmic fairness is to build systems with fairness properties that compose gracefully. A major effort and step towards this goal in data science has been the development offair representations which guarantee demographic parity ...