no code implementations • 24 May 2024 • Ş. İlker Birbil, Doğanay Özese, Mustafa Baydoğan
This paper introduces new variants of decision trees that can handle not only multi-target output but also the constraints among the targets.
no code implementations • 24 May 2024 • Jannis Kurtz, Ş. İlker Birbil, Dick den Hertog
The concept of counterfactual explanations (CE) has emerged as one of the important concepts to understand the inner workings of complex AI systems.
no code implementations • 3 Jul 2023 • Giovanni Cinà, Daniel Fernandez-Llaneza, Ludovico Deponte, Nishant Mishra, Tabea E. Röber, Sandro Pezzelle, Iacer Calixto, Rob Goedhart, Ş. İlker Birbil
Feature attribution methods have become a staple method to disentangle the complex behavior of black box models.
1 code implementation • 31 Jan 2023 • Barış Alparslan, Sinan Yildirim, Ş. İlker Birbil
We develop a novel generative statistical model for privately shared statistics, which exploits a useful distributional relation between the summary statistics of linear regression.
no code implementations • 5 Jan 2023 • Giovanni Cinà, Tabea E. Röber, Rob Goedhart, Ş. İlker Birbil
Despite valid concerns, we argue that existing criticism on the viability of post-hoc local explainability methods throws away the baby with the bathwater by generalizing a problem that is specific to image data.
Explainable Artificial Intelligence (XAI) Feature Importance +1
1 code implementation • 20 Oct 2022 • Esther Julien, Krzysztof Postek, Ş. İlker Birbil
One of the solution approaches to this class of problems is K-adaptability.
1 code implementation • 21 Apr 2021 • Tabea E. Röber, Adia C. Lumadjeng, M. Hakan Akyüz, Ş. İlker Birbil
The method returns a set of rules along with their optimal weights indicating the importance of each rule for learning.
1 code implementation • 5 Aug 2020 • Nurdan Kuru, Ş. İlker Birbil, Mert Gurbuzbalaban, Sinan Yildirim
The first algorithm is inspired by Polyak's heavy ball method and employs a smoothing approach to decrease the accumulated noise on the gradient steps required for differential privacy.
no code implementations • 5 Sep 2015 • Kamer Kaya, Figen Öztoprak, Ş. İlker Birbil, A. Taylan Cemgil, Umut Şimşekli, Nurdan Kuru, Hazal Koptagel, M. Kaan Öztürk
We propose HAMSI (Hessian Approximated Multiple Subsets Iteration), which is a provably convergent, second order incremental algorithm for solving large-scale partially separable optimization problems.
no code implementations • 3 Jun 2015 • Umut Şimşekli, Hazal Koptagel, Hakan Güldaş, A. Taylan Cemgil, Figen Öztoprak, Ş. İlker Birbil
For large matrix factorisation problems, we develop a distributed Markov Chain Monte Carlo (MCMC) method based on stochastic gradient Langevin dynamics (SGLD) that we call Parallel SGLD (PSGLD).