221 citations · 462 across the 6 of their papers we have counts for
6 papers
Individually Fair Gradient Boosting
Alexander Vargo, Fan Zhang, Mikhail Yurochkin +1
We consider the task of enforcing individual fairness in gradient boosting. Gradient boosting is a popular method for machine learning from tabular data, which arise often in appli…
Debiasing representations by removing unwanted variation due to protected attributes
Amanda Bower, Laura Niss, Yuekai Sun +1
We propose a regression-based approach to removing implicit biases in representations. On tasks where the protected attribute is observed, the method is statistically more efficien…
Efficient Algorithms for Maximum Likelihood Decoding in the Surface Code
Sergey Bravyi, Martin Suchara, Alexander Vargo
We describe two implementations of the optimal error correction algorithm known as the maximum likelihood decoder (MLD) for the 2D surface code with a noiseless syndrome extraction…
Simulation of rare events in quantum error correction
Sergey Bravyi, Alexander Vargo
We consider the problem of calculating the logical error probability for a stabilizer quantum code subject to random Pauli errors. To access the regime of large code distances wher…
Pretending to factor large numbers on a quantum computer
John A. Smolin, Graeme Smith, Alex Vargo
Shor's algorithm for factoring in polynomial time on a quantum computer\cite{Shor} gives an enormous advantage over all known classical factoring algorithm. We demonstrate how to f…
Approximation of real error channels by Clifford channels and Pauli measurements
Mauricio Gutiérrez, Lukas Svec, Alexander Vargo +1
The Gottesman-Knill theorem allows for the efficient simulation of stabilizer-based quantum error-correction circuits. Errors in these circuits are commonly modeled as depolarizing…