no code implementations • 24 May 2024 • Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas, Felix Zhou
To obtain this result, we design a replicable lifting framework inspired by Blanc, Lange, Malik, and Tan [2023] that transforms in a black-box manner efficient replicable PAC learners under the uniform marginal distribution over the Boolean hypercube to replicable PAC learners under any marginal distribution, with sample and time complexity that depends on a certain measure of the complexity of the distribution.
no code implementations • 21 Feb 2024 • Alkis Kalavasis, Amin Karbasi, Kasper Green Larsen, Grigoris Velegkas, Felix Zhou
Departing from the requirement of polynomial time algorithms, using the DP-to-Replicability reduction of Bun, Gaboardi, Hopkins, Impagliazzo, Lei, Pitassi, Sorrell, and Sivakumar [STOC, 2023], we show how to obtain a replicable algorithm for large-margin halfspaces with improved sample complexity with respect to the margin parameter $\tau$, but running time doubly exponential in $1/\tau^2$ and worse sample complexity dependence on $\epsilon$ than one of our previous algorithms.
no code implementations • 8 May 2019 • Sharib Ali, Felix Zhou, Christian Daul, Barbara Braden, Adam Bailey, Stefano Realdon, James East, Georges Wagnières, Victor Loschenov, Enrico Grisan, Walter Blondel, Jens Rittscher
Endoscopic artifacts are a core challenge in facilitating the diagnosis and treatment of diseases in hollow organs.
no code implementations • 15 Apr 2019 • Sharib Ali, Felix Zhou, Adam Bailey, Barbara Braden, James East, Xin Lu, Jens Rittscher
Given the widespread use of endoscopy in different clinical applications, we contend that the robust and reliable identification of such artifacts and the automated restoration of corrupted video frames is a fundamental medical imaging problem.