no code implementations • 3 Mar 2024 • Hyewon Jeong, Sarah Jabbour, Yuzhe Yang, Rahul Thapta, Hussein Mozannar, William Jongwon Han, Nikita Mehandru, Michael Wornow, Vladislav Lialin, Xin Liu, Alejandro Lozano, Jiacheng Zhu, Rafal Dariusz Kocielnik, Keith Harrigian, Haoran Zhang, Edward Lee, Milos Vukadinovic, Aparna Balagopalan, Vincent Jeanselme, Katherine Matton, Ilker Demirel, Jason Fries, Parisa Rashidi, Brett Beaulieu-Jones, Xuhai Orson Xu, Matthew McDermott, Tristan Naumann, Monica Agrawal, Marinka Zitnik, Berk Ustun, Edward Choi, Kristen Yeom, Gamze Gursoy, Marzyeh Ghassemi, Emma Pierson, George Chen, Sanjat Kanjilal, Michael Oberst, Linying Zhang, Harvineet Singh, Tom Hartvigsen, Helen Zhou, Chinasa T. Okolo
The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables.
no code implementations • 23 Feb 2024 • Ilker Demirel, Edward De Brouwer, Zeshan Hussain, Michael Oberst, Anthony Philippakis, David Sontag
Drawing causal inferences from observational studies (OS) requires unverifiable validity assumptions; however, one can falsify those assumptions by benchmarking the OS with experimental data from a randomized controlled trial (RCT).
no code implementations • 30 Jan 2023 • Zeshan Hussain, Ming-Chieh Shih, Michael Oberst, Ilker Demirel, David Sontag
Our approach is interpretable, allowing a practitioner to visualize which subgroups in the population lead to falsification of an observational study.
no code implementations • 9 May 2022 • Ilker Demirel, Yigit Yildirim, Cem Tekin
We demonstrate Fed-MoM-UCB's effectiveness against the baselines in the presence of Byzantine attacks via experiments.
no code implementations • 13 Dec 2021 • Ilker Demirel, Mehmet Ufuk Ozdemir, Cem Tekin
In this work, we tackle a different critical task through the lens of \textit{linear stochastic bandits}, where the aim is to keep the actions' outcomes close to a target level while respecting a \textit{two-sided} safety constraint, which we call \textit{leveling}.
1 code implementation • 26 Nov 2021 • Ilker Demirel, Ahmet Alparslan Celik, Cem Tekin
We propose ESCADA, a novel and generic multi-armed bandit (MAB) algorithm tailored for the leveling task, to make safe, personalized, and context-aware dose recommendations.