Kandemir, Melih

19 publications

TMLR 2025 Overcoming Non-Stationary Dynamics with Evidential Proximal Policy Optimization Abdullah Akgül, Gulcin Baykal, Manuel Haussmann, Melih Kandemir

L4DC 2024 Continual Learning of Multi-Modal Dynamics with External Memory Abdullah Akgül, Gozde Unal, Melih Kandemir

NeurIPS 2024 Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning Abdullah Akgül, Manuel Haußmann, Melih Kandemir

TMLR 2023 Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems Andreas Look, Barbara Rakitsch, Melih Kandemir, Jan Peters

ACML 2023 Estimation of Counterfactual Interventions Under Uncertainties Juliane Weilbach, Sebastian Gerwinn, Melih Kandemir, Martin Fraenzle

NeurIPS 2023 Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures Hamish Flynn, David Reeb, Melih Kandemir, Jan R Peters

TMLR 2023 Meta Continual Learning on Graphs with Experience Replay Altay Unal, Abdullah Akgül, Melih Kandemir, Gozde Unal

ICLR 2022 Evidential Turing Processes Melih Kandemir, Abdullah Akgül, Manuel Haussmann, Gozde Unal

NeurIPS 2022 Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs Çağatay Yıldız, Melih Kandemir, Barbara Rakitsch

L4DC 2022 Traversing Time with Multi-Resolution Gaussian Process State-Space Models Krista Longi, Jakob Lindinger, Olaf Duennbier, Melih Kandemir, Arto Klami, Barbara Rakitsch

AISTATS 2021 Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir

IJCAI 2019 Deep Active Learning with Adaptive Acquisition Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir

UAI 2019 Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir

NeurIPS 2018 Evidential Deep Learning to Quantify Classification Uncertainty Murat Sensoy, Lance Kaplan, Melih Kandemir

CVPR 2017 Variational Bayesian Multiple Instance Learning with Gaussian Processes Manuel Haussmann, Fred A. Hamprecht, Melih Kandemir

ECCV 2016 Gaussian Process Density Counting from Weak Supervision Matthias von Borstel, Melih Kandemir, Philip Schmidt, Madhavi K. Rao, Kumar T. Rajamani, Fred A. Hamprecht

ICML 2015 Asymmetric Transfer Learning with Deep Gaussian Processes Melih Kandemir

UAI 2014 Instance Label Prediction by Dirichlet Process Multiple Instance Learning Melih Kandemir, Fred A. Hamprecht

ECML-PKDD 2012 Unsupervised Inference of Auditory Attention from Biosensors Melih Kandemir, Arto Klami, Akos Vetek, Samuel Kaski