Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection. - Akif Burak Tosun; Melih Kandemir; Cenk Sokmensuer; Cigdem Gunduz Demir (Pattern Recognit., 2009)

Inferring object relevance from gaze in dynamic scenes. - Melih Kandemir; Veli-Matti Saarinen; Samuel Kaski (ETRA, 2010)

Automatic segmentation of colon glands using object-graphs. - Cigdem Gunduz Demir; Melih Kandemir; Akif Burak Tosun; Cenk Sokmensuer (Medical Image Anal., 2010)

Multitask Learning Using Regularized Multiple Kernel Learning. - Mehmet Gönen; Melih Kandemir; Samuel Kaski (ICONIP (2), 2011)

An augmented reality interface to contextual information. - Antti Ajanki; Mark Billinghurst; Hannes Gamper; Toni Järvenpää; Melih Kandemir; Samuel Kaski; Markus Koskela; Mikko Kurimo; Jorma Laaksonen; Kai Puolamäki; Teemu Ruokolainen; Timo Tossavainen (Virtual Real., 2011)

Learning relevance from natural eye movements in pervasive interfaces. - Melih Kandemir; Samuel Kaski (ICMI, 2012)

Unsupervised Inference of Auditory Attention from Biosensors. - Melih Kandemir; Arto Klami; Akos Vetek; Samuel Kaski (ECML/PKDD (2), 2012)

Learning Mental States from Biosignals. - Melih Kandemir (2013)

Multiple Instance Learning with Response-Optimized Random Forests. - Christoph N. Straehle; Melih Kandemir; Ullrich Köthe; Fred A. Hamprecht (ICPR, 2014)

Digital pathology: Multiple instance learning can detect Barrett's cancer. - Melih Kandemir; Annette Feuchtinger; Axel Walch; Fred A. Hamprecht (ISBI, 2014)

Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures. - Melih Kandemir; José C. Rubio; Ute Schmidt; Christian Wojek; Johannes Welbl; Björn Ommer; Fred A. Hamprecht (MICCAI (2), 2014)

Empowering Multiple Instance Histopathology Cancer Diagnosis by Cell Graphs. - Melih Kandemir; Chong Zhang 001; Fred A. Hamprecht (MICCAI (2), 2014)

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

Multi-task and multi-view learning of user state. - Melih Kandemir; Akos Vetek; Mehmet Gönen; Arto Klami; Samuel Kaski (Neurocomputing, 2014)

Detection of Retinopathy of Prematurity using multiple instance learning. - Priya Rani; Elagiri Ramalingam Rajkumar; Kumar T. Rajamani; Melih Kandemir; Digvijay Singh (ICACCI, 2015)

Asymmetric Transfer Learning with Deep Gaussian Processes. - Melih Kandemir (ICML, 2015)

Cell Event Detection in Phase-Contrast Microscopy Sequences from Few Annotations. - Melih Kandemir; Christian Wojek; Fred A. Hamprecht (MICCAI (3), 2015)

The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors. - Melih Kandemir; Fred A. Hamprecht (FE@NIPS, 2015)

Computer-aided diagnosis from weak supervision: A benchmarking study. - Melih Kandemir; Fred A. Hamprecht (Comput. Medical Imaging Graph., 2015)

Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals. - Jukka-Pekka Kauppi; Melih Kandemir; Veli-Matti Saarinen; Lotta Hirvenkari; Lauri Parkkonen; Arto Klami; Riitta Hari; Samuel Kaski (NeuroImage, 2015)

Variational Weakly Supervised Gaussian Processes. - Melih Kandemir; Manuel Haußmann; Ferran Diego; Kumar T. Rajamani; Jeroen van der Laak; Fred A. Hamprecht (BMVC, 2016)

Gaussian Process Density Counting from Weak Supervision. - Matthias von Borstel; Melih Kandemir; Philip Schmidt 001; Madhavi K. Rao; Kumar T. Rajamani; Fred A. Hamprecht (ECCV (1), 2016)

Variational Bayesian Multiple Instance Learning with Gaussian Processes. - Manuel Haußmann; Fred A. Hamprecht; Melih Kandemir (CVPR, 2017)

Prediction of active UE number with Bayesian neural networks for self-organizing LTE networks. - Omer Narmanlioglu; Engin Zeydan; Melih Kandemir; Yakup Tarik Kranda (NOF, 2017)

On Context-Aware DDoS Attacks Using Deep Generative Networks. - Gonca Gürsun; Murat Sensoy; Melih Kandemir (ICCCN, 2018)

Evidential Deep Learning to Quantify Classification Uncertainty. - Murat Sensoy; Lance M. Kaplan; Melih Kandemir (NeurIPS, 2018)

Sampling-Free Variational Inference of Bayesian Neural Nets. - Melih Kandemir; Manuel Haußmann; Fred A. Hamprecht (CoRR, 2018)

Supervising topic models with Gaussian processes. - Melih Kandemir; Taygun Kekeç; Reyyan Yeniterzi (Pattern Recognit., 2018)

Variational closed-Form deep neural net inference. - Melih Kandemir (Pattern Recognit. Lett., 2018)

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

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

Bayesian Prior Networks with PAC Training. - Manuel Haußmann; Sebastian Gerwinn; Melih Kandemir (CoRR, 2019)

Differential Bayesian Neural Nets. - Andreas Look; Melih Kandemir (CoRR, 2019)

Deterministic Inference of Neural Stochastic Differential Equations. - Andreas Look; Chen Qiu 001; Maja Rudolph; Jan Peters 001; Melih Kandemir (CoRR, 2020)

Differentiable Implicit Layers. - Andreas Look; Simona Doneva; Melih Kandemir; Rainer Gemulla; Jan Peters 001 (CoRR, 2020)

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

Inferring the Structure of Ordinary Differential Equations. - Juliane Weilbach; Sebastian Gerwinn; Christian Weilbach; Melih Kandemir (CoRR, 2021)

Evidential Turing Processes. - Melih Kandemir; Abdullah Akgül; Manuel Haußmann; Gozde Unal (ICLR, 2022)

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

Learning interacting dynamical systems with latent Gaussian process ODEs. - Çagatay Yildiz; Melih Kandemir; Barbara Rakitsch (NeurIPS, 2022)

PAC-Bayesian lifelong learning for multi-armed bandits. - Hamish Flynn; David Reeb; Melih Kandemir; Jan Peters 001 (Data Min. Knowl. Discov., 2022)

Estimation of Counterfactual Interventions under Uncertainties. - Juliane Weilbach; Sebastian Gerwinn; Melih Kandemir; Martin Fränzle (ACML, 2023)

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

PAC-Bayesian Soft Actor-Critic Learning. - Bahareh Tasdighi; Abdullah Akgül; Kenny Kazimirzak Brink; Melih Kandemir (CoRR, 2023)

BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits. - Nicklas Werge; Abdullah Akgül; Melih Kandemir (CoRR, 2023)

Sampling-Free Probabilistic Deep State-Space Models. - Andreas Look; Melih Kandemir; Barbara Rakitsch; Jan Peters 001 (CoRR, 2023)

Demystifying the Myths and Legends of Nonconvex Convergence of SGD. - Aritra Dutta; El Houcine Bergou; Soumia Boucherouite; Nicklas Werge; Melih Kandemir; Xin Li 022 (CoRR, 2023)

Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems. - Andreas Look; Barbara Rakitsch; Melih Kandemir; Jan Peters 001 (Trans. Mach. Learn. Res., 2023)

Meta Continual Learning on Graphs with Experience Replay. - Altay Unal; Abdullah Akgül; Melih Kandemir; Gozde Unal (Trans. Mach. Learn. Res., 2023)

A Deterministic Approximation to Neural SDEs. - Andreas Look; Melih Kandemir; Barbara Rakitsch; Jan Peters 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison. - Hamish Flynn; David Reeb; Melih Kandemir; Jan Peters 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

PAC-Bayesian Soft Actor-Critic Learning. - Bahareh Tasdighi; Abdullah Akgül; Manuel Haussmann; Kenny Kazimirzak Brink; Melih Kandemir (AABI, 2024)

Continual learning of multi-modal dynamics with external memory. - Abdullah Akgül; Gozde Unal; Melih Kandemir (L4DC, 2024)

Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning. - Abdullah Akgül; Manuel Haussmann; Melih Kandemir (NeurIPS, 2024)

Probabilistic Actor-Critic: Learning to Explore with PAC-Bayes Uncertainty. - Bahareh Tasdighi; Nicklas Werge; Yi-Shan Wu 003; Melih Kandemir (CoRR, 2024)

Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting. - Busra Asan; Abdullah Akgül; Alper Ünal; Melih Kandemir; Gozde Unal (CoRR, 2024)

Exploring Pessimism and Optimism Dynamics in Deep Reinforcement Learning. - Bahareh Tasdighi; Nicklas Werge; Yi-Shan Wu 003; Melih Kandemir (CoRR, 2024)

Disentanglement with Factor Quantized Variational Autoencoders. - Gulcin Baykal; Melih Kandemir; Gozde Unal (CoRR, 2024)

EdVAE: Mitigating codebook collapse with evidential discrete variational autoencoders. - Gulcin Baykal; Melih Kandemir; Gozde Unal (Pattern Recognit., 2024)

In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review. - Amelia Jiménez-Sánchez; Natalia Rozalia Avlona; Sarah de Boer; Víctor M. Campello; Aasa Feragen; Enzo Ferrante; Melanie Ganz; Judy Wawira Gichoya; Camila González; Steff Groefsema; Alessa Hering; Adam Hulman; et al. (FAccT, 2025)

Improving Plasticity in Non-stationary Reinforcement Learning with Evidential Proximal Policy Optimization. - Abdullah Akgül; Gulcin Baykal; Manuel Haußmann; Melih Kandemir (CoRR, 2025)

Deep Actor-Critics with Tight Risk Certificates. - Bahareh Tasdighi; Manuel Haussmann; Yi-Shan Wu 003; Andrés R. Masegosa; Melih Kandemir (CoRR, 2025)

ObjectRL: An Object-Oriented Reinforcement Learning Codebase. - Gulcin Baykal; Abdullah Akgül; Manuel Haussmann; Bahareh Tasdighi; Nicklas Werge; Yi-Shan Wu 003; Melih Kandemir (CoRR, 2025)

Directional Ensemble Aggregation for Actor-Critics. - Nicklas Werge; Yi-Shan Wu 003; Bahareh Tasdighi; Melih Kandemir (CoRR, 2025)