2 code implementations • NeurIPS 2023 • Elita A. Lobo, Cyrus Cousins, Yair Zick, Marek Petrik
The percentile criterion is approximately solved by constructing an \emph{ambiguity set} that contains the true model with high probability and optimizing the policy for the worst model in the set.
no code implementations • 29 Feb 2024 • Cyrus Cousins, I. Elizabeth Kumar, Suresh Venkatasubramanian
In fair machine learning, one source of performance disparities between groups is over-fitting to groups with relatively few training samples.
no code implementations • 6 Feb 2023 • Cyrus Cousins, Vignesh Viswanathan, Yair Zick
This is surprising since for the simpler classes of bivalued additive valuations and binary submodular valuations, MNW allocations are known to be envy free up to any good (EFX).
no code implementations • NeurIPS 2021 • Shahrzad Haddadan, Yue Zhuang, Cyrus Cousins, Eli Upfal
While the cooling schedule in these algorithms is adaptive, the mean estimation computations use MCMC as a black-box to draw approximate samples.
no code implementations • NeurIPS 2021 • Cyrus Cousins
Unfortunately, many machine-learning problems are more naturally cast as loss minimization tasks, rather than utility maximization, which complicates direct application of welfare-centric methods to fair machine learning.
no code implementations • NeurIPS 2020 • Cyrus Cousins, Matteo Riondato
We introduce the use of empirical centralization to derive novel practical, probabilistic, sample-dependent bounds to the Supremum Deviation (SD) of empirical means of functions in a family from their expectations.
1 code implementation • 16 Jun 2020 • Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin, Matteo Riondato
To show the practical use of MCRapper, we employ it to develop an algorithm TFP-R for the task of True Frequent Pattern (TFP) mining.
no code implementations • 18 Dec 2018 • Clayton Sanford, Cyrus Cousins, Eli Upfal
We frame the problem of selecting an optimal audio encoding scheme as a supervised learning task.