Publications

2026

  1. What Information Buys in Active Inference
    M. Kandemir and M.M. Çelikok
    arXiv Preprint, 2026
  3. Let CSP Be Your ANCHOR: Adaptive Crystal Search over Frozen Structure Priors
    E.L. Hovmand, J. Elsborg, M. Kandemir, and A. Bhowmik
    arXiv Preprint, 2026
  4. Hitting Time Isomorphism for Multi-Stage Planning with Foundation Policies
    M.V. Boock, A. Akgül, M.M. Çelikok, and M. Kandemir
    In Neural Information Processing Systems, 2026
  5. Fitted Q-Iteration with Continuous Actions and Adaptive Data: Smoothed Coverage and Minimax Rates
    M. Haussmann, M.M. Çelikok, and M. Kandemir
    arXiv Preprint, 2026
  6. Policy-based Tuning of Autoregressive Image Models with Instance- and Distribution-Level Rewards
    O.B Baran, M. Kandemir, and R.G. Cinbis
    In European Conference on Computer Vision, 2026
  8. Distributional Active Inference
    A. Akgül, G. Baykal, M. Haußmann, M. M. Çelikok, and M. Kandemir
    In International Conference on Machine Learning, 2026
  9. Bridging the Performance-gap between Target-free and Target-based Reinforcement Learning
    T. Vincent, Y. Tripathi, T. Faust, A. Akgül, Y. Oren, M. Kandemir, J. Peters, and C. d’Eramo
    In International Conference on Learning Representations, 2026
  10. Weighted Sequential Bayesian Inference for Non-Stationary Linear Contextual Bandits
    N. Werge, Y.S. Wu, A. Akgul, and M. Kandemir
    In International Conference on Uncertainty in Artificial Intelligence, 2026

2025

  1. In the picture: Medical imaging datasets, artifacts, and their living review
    A. Jiménez-Sánchez, N. Avlona, S.D. Boer, V.M. Campello, A. Feragen, E. Ferrante, M. Ganz, J.W. Gichoya, C. Gonzalez, S. Groefsema, A. Hering, A. Hulman, L. Joskowicz, D. Juodelyte, M. Kandemir, T. Kooi, J.d.P. Lérida, L.Y. Li, A. Pacheco, T. Rädsch, M. Reyes, T. Sourget, B.V. Ginneken, D. Wen, N. Weng, J.J. Xu, H.D. Zajaç, M.A. Zuluaga, and V. Cheplygina
    In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, 2025
  2. Overcoming Non-stationary Dynamics with Evidential Proximal Policy Optimization
    A. Akgül, G. Baykal, M. Haußmann, and M. Kandemir
    Transactions on Machine Learning Research, 2025
  3. Adaptive Ensemble Aggregation for Actor-Critics
    N. Werge, Y.S. Wu, B. Tasdighi, and M. Kandemir
    arXiv Preprint, 2025
  4. ObjectRL: An Object-Oriented Reinforcement Learning Codebase
    G. Baykal, A. Akgül, M. Haussmann, B. Tasdighi, N. Werge, Y.S. Wu, and M. Kandemir
    arXiv Preprint, 2025
  5. Deep Actor-Critics with Tight Risk Certificates
    B. Tasdighi, M. Haußmann, Y.S. Wu, Masegosa. A.R., and M. Kandemir
    In European Workshop on Reinforcement Learning, 2025
  6. Improving Actor-Critic Training with Steerable Action-Value Approximation Errors
    B. Tasdighi, N. Werge, Y.S. Wu, and M. Kandemir
    In European Conference on Artificial Intelligence, 2025
  7. Deep Exploration with PAC-Bayes
    B. Tasdighi, M. Haussmann, N. Werge, Y. Wu, and M. Kandemir
    In European Conference on Artificial Intelligence, 2025
  8. Disentanglement with Factor Quantized Variational Autoencoders
    G. Baykal, M. Kandemir, and G. Unal
    Neurocomputing, 2025

2024

  1. Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning
    A. Akgul, M. Haussmann, and M. Kandemir
    In Neural Information Processing Systems, 2024
  2. Continual Learning of Multi-modal Dynamics with External Memory
    A. Akgül, G. Unal, and M. Kandemir
    In Learning for Dynamics and Control Conference (L4DC), 2024
  3. Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting
    B. Asan, A. Akgül, A. Unal, M. Kandemir, and G. Unal
    In Workshop on Tackling Climate Change with Machine Learning, 2024
  4. EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoders
    G. Baykal, M. Kandemir, and G. Unal
    Pattern Recognition, 2024
  5. PAC-Bayesian Soft Actor-Critic Learning
    B. Tasdighi, A. Akgül, K.K. Brink, and M. Kandemir
    In Advances in Approximate Bayesian Inference Symposium, 2024

2023

  1. Sampling-Free Probabilistic Deep State-Space Models
    A. Look, M. Kandemir, B. Rakitsch, and J. Peters
    arXiv Preprint, 2023
  2. Demystifying the Myths and Legends of Nonconvex Convergence of SGD
    A. Dutta, E.H. Bergou, S. Boucherouite, N. Werge, M. Kandemir, and X. Li
    arXiv Preprint, 2023
  3. Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures
    H. Flynn, D. Reeb, M. Kandemir, and J. Peters
    In Neural Information Processing Systems, 2023
  4. Meta Continual Learning on Graphs with Experience Replay
    A. Unal, A. Akgül, M. Kandemir, and G. Unal
    Transactions on Machine Learning Research, 2023
  5. Estimation of Counterfactual Interventions under Uncertainties
    J. Weilbach, S. Gerwinn, M. Kandemir, and M. Fraenzle
    In Asian Conference on Machine Learning, 2023
  6. PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison
    H. Flynn, D. Reeb, M. Kandemir, and J. Peters
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
  7. ALReg: Registration of 3D Point Clouds Using Active Learning
    Y.H. Sahin, O. Karabacak, M. Kandemir, and G. Unal
    MDPI Applied Sciences, 2023
  8. Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems
    A. Look, B. Rakitsch, M. Kandemir, and J. Peters
    Transactions on Machine Learning Research, 2023

2022

  1. Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs
    C. Yildiz, M. Kandemir, and B. Rakitsch
    In Neural Information Processing Systems, 2022
  2. Evidential Turing Processes
    M. Kandemir, A. Akgül, M. Haussmann, and G. Unal
    In International Conference on Learning Representations, 2022
  3. A Deterministic Approximation to Neural SDEs
    A. Look, M. Kandemir, B. Rakitsch, and J. Peters
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
  4. Traversing Time with Multi-Resolution Gaussian Process State-Space Models
    K. Longi, J. Lindinger, O. Duennbier, M. Kandemir, A. Klami, and B Rakitsch
    In Learning for Dynamics and Control Conference, 2022
  5. PAC-Bayesian lifelong learning for multi-armed bandits
    H. Flynn, D. Reeb, M. Kandemir, and J. Peters
    Data Mining and Knowledge Discovery, 2022

2021

  1. Inferring the Structure of Ordinary Differential Equations
    J. Weilbach, S. Gerwinn, C. Weilbach, and M. Kandemir
    In ICML Time Series Workshop, 2021
  2. Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
    M. Haussmann, S. Gerwinn, A. Look, B. Rakitsch, and M. Kandemir
    In International Conference on Artificial Intelligence and Statistics, 2021

2020

  1. Differentiable Implicit Layers
    A. Look, S. Doneva, M. Kandemir, R. Gemulla, and J. Peters
    In NeurIPS ML for Engineering Workshop, 2020
  2. Bayesian Evidential Deep Learning with PAC Regularization
    M. Haussmann, S. Gerwinn, and M. Kandemir
    In Advances in Approximate Bayesian Inference Symposium, 2020

2019

  1. Deep Active Learning with Adaptive Acquisition
    M. Haußmann, F.A. Hamprecht, and M. Kandemir
    In International Joint Conference on Artificial Intelligence, 2019
  2. Differential Bayesian Neural Nets
    A. Look and M. Kandemir
    In NeurIPS Bayesian Deep Learning Workshop, 2019
  3. Sampling-Free Variational Inference of Bayesian Neural Nets with Variance Backpropagation
    M. Haussmann, F.A. Hamprecht, and M. Kandemir
    In International Conference on Uncertainty in Artificial Intelligence, 2019

2018

  1. Variational Closed-Form Deep Neural Net Inference
    M. Kandemir
    Pattern Recognition Letters, 2018
  2. Evidential Deep Learning to Quantify Classification Uncertainty
    M. Sensoy, L. Kaplan, and M. Kandemir
    In Neural Information Processing Systems, 2018
  3. On Context-Aware DDoS Attacks using Deep Generative Networks
    G. Gürsun, M. Sensoy, and M. Kandemir
    In International Conference on Computer Communication and Networks (ICCCN), 2018
  4. Supervising Topic Models with Gaussian Processes
    M. Kandemir, T. Kekeç, and R. Yeniterzi
    Pattern Recognition, 2018

2017

  1. Variational Bayesian Multiple Instance Learning with Gaussian Processes
    M. Haußmann, F.A. Hamprecht, and M. Kandemir
    In Computer Vision and Pattern Recognition, 2017
  2. Prediction of Active UE Number with Bayesian Neural Networks for Self-Organizing LTE Networks
    O. Narmanlioglu, E. Zeydan, M. Kandemir, and T. Kranda
    In International Conference on the Network of the Future (NOF), 2017

2016

  1. Gaussian Process Density Counting from Weak Supervision
    M.v. Borstel, M. Kandemir, P. Schmidt, M.K. Rao, K. Rajamani, and F.A. Hamprecht
    In European Conference on Computer Vision, 2016
  2. Variational Weakly Supervised Gaussian Processes
    M. Kandemir, M. Haußmann, F. Diego, K. Rajamani, J. Van Der Laak, and F.A. Hamprecht
    In British Machine Vision Conference, 2016
  3. Multiple Instance Learning: Robust Validation on Retinopathy of Prematurity
    P. Rani, R. Elagiri, K. Rajamani, M. Kandemir, and D. Singh
    International Journal of Control Theory and Applicatioms, 2016

2015

  1. Computer-Aided Diagnosis from Weak Supervision: A Benchmarking Study
    M. Kandemir and F.A. Hamprecht
    Computerized medical imaging and graphics, 2015
  2. Cell Event Detection in Phase-Contrast Microscopy Sequences from Few Annotations
    M. Kandemir, C. Wojek, and F.A. Hamprecht
    In International Conference on Medical Image Computing and Computer Assisted Interventions, 2015
  3. Detection of Retinopathy of Prematurity using Multiple Instance Learning
    P. Rani, E. Ramalingam Rajkumar, K. Rajamani, M. Kandemir, and D. Singh
    In International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2015
  4. Towards Brain-Activity-Controlled Information Retrieval: Decoding Image Relevance from MEG Signals
    J.P. Kauppi, M. Kandemir, V.M. Saarinen, L. Hirvenkari, L. Parkkonen, A. Klami, R. Hari, and S. Kaski
    NeuroImage, 2015
  5. Asymmetric Transfer Learning with Deep Gaussian Processes
    M. Kandemir
    In International Conference on Machine Learning, 2015
  6. The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors
    M. Kandemir and F. A. Hamprecht
    In Feature Extraction: Modern Questions and Challenges, 2015

2014

  1. Empowering Multiple Instance Histopathology Cancer Diagnosis by Cell Graphs
    M. Kandemir, C. Zhang, and F.A. Hamprecht
    In International Conference on Medical Image Computing and Computer Assisted Interventions, 2014
  2. Digital Pathology: Multiple Instance Learning can Detect Barrett’s Cancer
    M. Kandemir, A. Feuchtinger, A. Walch, and F.A. Hamprecht
    In International Symposium on Biomedical Imaging (ISBI), 2014
  3. Instance Label Prediction by Dirichlet Process Multiple Instance Learning
    M. Kandemir and F. A. Hamprecht
    In International Conference on Uncertainty in Artificial Intelligence, 2014
  4. Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures
    M. Kandemir, J.C. Rubio, U. Schmidt, C. Wojek, J. Welbl, B. Ommer, and F.A. Hamprecht
    In International Conference on Medical Image Computing and Computer Assisted Interventions, 2014
  5. Multiple Instance Learning with Response-Optimized Random Forests
    C. Straehle, M. Kandemir, U. Koethe, and F.A. Hamprecht
    In International Conference on Pattern Recognition, 2014
  6. Multi-Task and Multi-View Learning of User State
    M. Kandemir, A. Vetek, M. Gönen, A. Klami, and S. Kaski
    Neurocomputing, 2014

2013

2012

  1. Learning Relevance from Natural Eye Movements in Pervasive Interfaces
    M. Kandemir and S. Kaski
    In International Conference on Multimodal Interaction, 2012
  2. Unsupervised Inference of Auditory Attention from Biosensors
    M. Kandemir, A. Klami, A. Vetek, and S. Kaski
    In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2012

2011

  1. Multitask learning using regularized multiple kernel learning
    M. Gönen, M. Kandemir, and S. Kaski
    In International Conference on Neural Information Processing, 2011
  2. An Augmented Reality Interface to Contextual Information
    A. Ajanki, M. Billinghurst, H. Gamper, T. Järvenpää, M. Kandemir, S. Kaski, M. Koskela, M. Kurimo, J. Laaksonen, K. Puolamäki, and others
    Virtual reality, 2011

2010

  1. Contextual information access with augmented reality
    A. Ajanki, M. Billinghurst, T. Järvenpää, M. Kandemir, S. Kaski, M. Koskela, M. Kurimo, J. Laaksonen, K. Puolamäki, T. Ruokolainen, and others
    In International Workshop on Machine Learning for Signal Processing, 2010
  2. Automatic segmentation of colon glands using object-graphs
    C. Gunduz-Demir, M. Kandemir, A.B. Tosun, and C. Sokmensuer
    Medical image analysis, 2010
  3. Inferring Object Relevance from Gaze in Dynamic Scenes
    M. Kandemir, V.M. Saarinen, and S. Kaski
    In Symposium on Eye-Tracking Research & Applications, 2010

2009

  1. Ubiquitous contextual information access with proactive retrieval and augmentation
    A. Ajanki, M. Billinghurst, M. Kandemir, S. Kaski, M. Koskela, M. Kurimo, J. Laaksonen, K. Puolamäki, and T. Tossavainen
    Helsinki University of Technology, 2009
  2. Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection
    A.B. Tosun, M. Kandemir, C. Sokmensuer, and C. Gunduz-Demir
    Pattern Recognition, 2009

2008

  1. Segmentation of Colon Glands by Object Graphs
    M. Kandemir
    Bilkent University, 2008

2007

  1. A Framework for Real-Time Animation of Liquid-Rigid Body Interaction
    M. Kandemi̇r, T. Çapin, and B. Özgüç
    2007