no code implementations • ICML 2020 • Himabindu Lakkaraju, Nino Arsov, Osbert Bastani
As machine learning black boxes are increasingly being deployed in real-world applications, there has been a growing interest in developing post hoc explanations that summarize the behaviors of these black box models.
no code implementations • 12 Nov 2020 • Himabindu Lakkaraju, Nino Arsov, Osbert Bastani
To the best of our knowledge, this work makes the first attempt at generating post hoc explanations that are robust to a general class of adversarial perturbations that are of practical interest.
no code implementations • 26 Nov 2019 • Nino Arsov, Georgina Mirceva
In addition, we give examples of real-world machine learning problems on networks in which the embedding is critical in order to maximize the predictive performance of the machine learning task.
no code implementations • 26 Nov 2019 • Nino Arsov, Goran Velinov, Aleksandar S. Dimovski, Bojana Koteska, Dragan Sahpaski, Margina Kon-Popovska
In this paper we present a novel approach for finding an optimal horizontally partitioned schema that manifests a minimal total execution cost of a given database workload.
no code implementations • 26 Nov 2019 • Nino Arsov, Milan Dukovski, Blagoja Evkoski, Stefan Cvetkovski
In the last decade, many diverse advances have occurred in the field of information extraction from data.
no code implementations • 3 Mar 2019 • Nino Arsov, Martin Pavlovski, Ljupco Kocarev
To that end, in this paper, we derive two stability notions for decision trees and logistic regression: hypothesis and pointwise hypothesis stability.
no code implementations • 26 Jan 2019 • Nino Arsov, Martin Pavlovski, Ljupco Kocarev
We show that the hypothesis stability of stacking is a product of the hypothesis stability of each of the base models and the combiner.