no code implementations • 29 Apr 2022 • Pablo Mosteiro, Emil Rijcken, Kalliopi Zervanou, Uzay Kaymak, Floortje Scheepers, Marco Spruit
Violence risk assessment in psychiatric institutions enables interventions to avoid violence incidents.
no code implementations • 28 Apr 2022 • Pablo Mosteiro, Emil Rijcken, Kalliopi Zervanou, Uzay Kaymak, Floortje Scheepers, Marco Spruit
We explore conventional and deep machine learning methods to assess violence risk in psychiatric patients using practitioner notes.
no code implementations • 13 Jan 2022 • Reza Refaei Afshar, Yingqian Zhang, Joaquin Vanschoren, Uzay Kaymak
Automated RL provides a framework in which different components of RL including MDP modeling, algorithm selection and hyper-parameter optimization are modeled and defined automatically.
no code implementations • 25 Apr 2020 • Reza Refaei Afshar, Yingqian Zhang, Murat Firat, Uzay Kaymak
This paper proposes a Deep Reinforcement Learning (DRL) approach for solving knapsack problem.
no code implementations • 21 Apr 2020 • Jason Rhuggenaath, Alp Akcay, Yingqian Zhang, Uzay Kaymak
In this paper, we study a slate bandit problem where the function that determines the slate-level reward is non-separable: the optimal value of the function cannot be determined by learning the optimal action for each slot.
no code implementations • 29 Jan 2020 • Sicui Zhang, Laura Genga, Hui Yan, Xudong Lu, Huilong Duan, Uzay Kaymak
This affects the quality of the provided diagnostics, especially when there exists some tolerance with respect to reasonably small violations, and hampers the flexibility of the process.
1 code implementation • Journal of Biomedical Informatics 2019 • Peipei Chen, Wei Dong, Xudong, Uzay Kaymak, Kunlun He, Zhengxing Huang
We propose a novel hybrid model bridging multi-task deep learning and K-nearest neighbors (KNN) for ITE estimation.
Ranked #3 on Causal Inference on IHDP
no code implementations • 17 Jul 2019 • Paulo R. de O. da Costa, Alp Akcay, Yingqian Zhang, Uzay Kaymak
We propose a Domain Adversarial Neural Network (DANN) approach to learn domain-invariant features that can be used to predict the RUL in the target domain.