Melih Kandemir

85 papers A* 8A 6B 4C 1Journal 51Unranked 14

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Disentanglement with factor quantized variational autoencoders. Neurocomputing Gulcin Baykal, Melih Kandemir, Gozde Unal
2026	J	`jnl`	Distributional Active Inference. CoRR Abdullah Akgül, Gulcin Baykal, Manuel Haußmann, Mustafa Mert Çelikok, Melih Kandemir
2026	J	`jnl`	Policy-based Tuning of Autoregressive Image Models with Instance- and Distribution-Level Rewards. CoRR Orhun Bugra Baran, Melih Kandemir, Ramazan Gokberk Cinbis
2025	J	`jnl`	Deep Actor-Critics with Tight Risk Certificates. CoRR Bahareh Tasdighi, Manuel Haussmann, Yi-Shan Wu, Andrés R. Masegosa, Melih Kandemir
2025	A	`conf`	Deep Exploration with PAC-Bayes. ECAI Bahareh Tasdighi, Manuel Haussmann, Nicklas Werge, Yi-Shan Wu, Melih Kandemir
2025	J	`jnl`	Directional Ensemble Aggregation for Actor-Critics. CoRR Nicklas Werge, Yi-Shan Wu, Bahareh Tasdighi, Melih Kandemir
2025	A	`conf`	Improving Actor-Critic Training with Steerable Action-Value Approximation Errors. ECAI Bahareh Tasdighi, Nicklas Werge, Yi-Shan Wu, Melih Kandemir
2025	J	`jnl`	Improving Plasticity in Non-stationary Reinforcement Learning with Evidential Proximal Policy Optimization. CoRR Abdullah Akgül, Gulcin Baykal, Manuel Haußmann, Melih Kandemir
2025	—	`conf`	In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review. FAccT 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, Leo Joskowicz, Dovile Juodelyte, Melih Kandemir, Thijs Kooi, Jorge del Pozo Lérida, Livie Yumeng Li, André G. C. Pacheco, Tim Rädsch, Mauricio Reyes, Théo Sourget, Bram van Ginneken, David Wen, Nina Weng, Jack Junchi Xu, Hubert Dariusz Zajac, Maria A. Zuluaga, Veronika Cheplygina
2025	J	`jnl`	In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review. CoRR 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, Leo Joskowicz, Dovile Juodelyte, Melih Kandemir, Thijs Kooi, Jorge del Pozo Lérida, Livie Yumeng Li, André G. C. Pacheco, Tim Rädsch, Mauricio Reyes, Théo Sourget, Bram van Ginneken, David Wen, Nina Weng, Jack Junchi Xu, Hubert Dariusz Zajac, Maria A. Zuluaga, Veronika Cheplygina
2025	J	`jnl`	ObjectRL: An Object-Oriented Reinforcement Learning Codebase. CoRR Gulcin Baykal, Abdullah Akgül, Manuel Haussmann, Bahareh Tasdighi, Nicklas Werge, Yi-Shan Wu, Melih Kandemir
2025	J	`jnl`	Overcoming Non-stationary Dynamics with Evidential Proximal Policy Optimization. Trans. Mach. Learn. Res. Abdullah Akgül, Gulcin Baykal, Manuel Haussmann, Melih Kandemir
2024	J	`jnl`	Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting. CoRR Busra Asan, Abdullah Akgül, Alper Ünal, Melih Kandemir, Gozde Unal
2024	—	`conf`	Continual learning of multi-modal dynamics with external memory. L4DC Abdullah Akgül, Gozde Unal, Melih Kandemir
2024	A*	`conf`	Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning. NeurIPS Abdullah Akgül, Manuel Haussmann, Melih Kandemir
2024	J	`jnl`	Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning. CoRR Abdullah Akgül, Manuel Haußmann, Melih Kandemir
2024	J	`jnl`	Disentanglement with Factor Quantized Variational Autoencoders. CoRR Gulcin Baykal, Melih Kandemir, Gozde Unal
2024	J	`jnl`	EdVAE: Mitigating codebook collapse with evidential discrete variational autoencoders. Pattern Recognit. Gulcin Baykal, Melih Kandemir, Gozde Unal
2024	J	`jnl`	Exploring Pessimism and Optimism Dynamics in Deep Reinforcement Learning. CoRR Bahareh Tasdighi, Nicklas Werge, Yi-Shan Wu, Melih Kandemir
2024	—	`conf`	PAC-Bayesian Soft Actor-Critic Learning. AABI Bahareh Tasdighi, Abdullah Akgül, Manuel Haussmann, Kenny Kazimirzak Brink, Melih Kandemir
2024	J	`jnl`	Probabilistic Actor-Critic: Learning to Explore with PAC-Bayes Uncertainty. CoRR Bahareh Tasdighi, Nicklas Werge, Yi-Shan Wu, Melih Kandemir
2023	J	`jnl`	A Deterministic Approximation to Neural SDEs. IEEE Trans. Pattern Anal. Mach. Intell. Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters
2023	J	`jnl`	BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits. CoRR Nicklas Werge, Abdullah Akgül, Melih Kandemir
2023	J	`jnl`	Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems. CoRR Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters
2023	J	`jnl`	Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems. Trans. Mach. Learn. Res. Andreas Look, Barbara Rakitsch, Melih Kandemir, Jan Peters
2023	J	`jnl`	Demystifying the Myths and Legends of Nonconvex Convergence of SGD. CoRR Aritra Dutta, El Houcine Bergou, Soumia Boucherouite, Nicklas Werge, Melih Kandemir, Xin Li
2023	J	`jnl`	EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoders. CoRR Gulcin Baykal, Melih Kandemir, Gozde Unal
2023	C	`conf`	Estimation of Counterfactual Interventions under Uncertainties. ACML Juliane Weilbach, Sebastian Gerwinn, Melih Kandemir, Martin Fränzle
2023	J	`jnl`	Estimation of Counterfactual Interventions under Uncertainties. CoRR Juliane Weilbach, Sebastian Gerwinn, Melih Kandemir, Martin Fränzle
2023	A*	`conf`	Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures. NeurIPS Hamish Flynn, David Reeb, Melih Kandemir, Jan R. Peters
2023	J	`jnl`	Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures. CoRR Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters
2023	J	`jnl`	Meta Continual Learning on Graphs with Experience Replay. Trans. Mach. Learn. Res. Altay Unal, Abdullah Akgül, Melih Kandemir, Gozde Unal
2023	J	`jnl`	PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison. IEEE Trans. Pattern Anal. Mach. Intell. Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters
2023	J	`jnl`	PAC-Bayesian Soft Actor-Critic Learning. CoRR Bahareh Tasdighi, Abdullah Akgül, Kenny Kazimirzak Brink, Melih Kandemir
2023	J	`jnl`	Sampling-Free Probabilistic Deep State-Space Models. CoRR Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters
2022	J	`jnl`	Continual Learning of Multi-modal Dynamics with External Memory. CoRR Abdullah Akgül, Gozde Unal, Melih Kandemir
2022	A*	`conf`	Evidential Turing Processes. ICLR Melih Kandemir, Abdullah Akgül, Manuel Haußmann, Gozde Unal
2022	J	`jnl`	Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs. CoRR Çagatay Yildiz, Melih Kandemir, Barbara Rakitsch
2022	A*	`conf`	Learning interacting dynamical systems with latent Gaussian process ODEs. NeurIPS Çagatay Yildiz, Melih Kandemir, Barbara Rakitsch
2022	J	`jnl`	PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison. CoRR Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters
2022	J	`jnl`	PAC-Bayesian Lifelong Learning For Multi-Armed Bandits. CoRR Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters
2022	J	`jnl`	PAC-Bayesian lifelong learning for multi-armed bandits. Data Min. Knowl. Discov. Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters
2022	—	`conf`	Traversing Time with Multi-Resolution Gaussian Process State-Space Models. L4DC Krista Longi, Jakob Lindinger, Olaf Dünnbier, Melih Kandemir, Arto Klami, Barbara Rakitsch
2021	J	`jnl`	Evidential Turing Processes. CoRR Melih Kandemir, Abdullah Akgül, Manuel Haußmann, Gozde Unal
2021	J	`jnl`	Inferring the Structure of Ordinary Differential Equations. CoRR Juliane Weilbach, Sebastian Gerwinn, Christian Weilbach, Melih Kandemir
2021	A	`conf`	Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. AISTATS Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir
2021	J	`jnl`	Traversing Time with Multi-Resolution Gaussian Process State-Space Models. CoRR Krista Longi, Jakob Lindinger, Olaf Dünnbier, Melih Kandemir, Arto Klami, Barbara Rakitsch
2020	J	`jnl`	Deterministic Inference of Neural Stochastic Differential Equations. CoRR Andreas Look, Chen Qiu, Maja Rudolph, Jan Peters, Melih Kandemir
2020	J	`jnl`	Differentiable Implicit Layers. CoRR Andreas Look, Simona Doneva, Melih Kandemir, Rainer Gemulla, Jan Peters
2020	J	`jnl`	Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. CoRR Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir
2019	J	`jnl`	Bayesian Prior Networks with PAC Training. CoRR Manuel Haußmann, Sebastian Gerwinn, Melih Kandemir
2019	A*	`conf`	Deep Active Learning with Adaptive Acquisition. IJCAI Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir
2019	J	`jnl`	Deep Active Learning with Adaptive Acquisition. CoRR Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir
2019	J	`jnl`	Differential Bayesian Neural Nets. CoRR Andreas Look, Melih Kandemir
2019	A	`conf`	Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation. UAI Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir
2018	A*	`conf`	Evidential Deep Learning to Quantify Classification Uncertainty. NeurIPS Murat Sensoy, Lance M. Kaplan, Melih Kandemir
2018	J	`jnl`	Evidential Deep Learning to Quantify Classification Uncertainty. CoRR Murat Sensoy, Melih Kandemir, Lance M. Kaplan
2018	B	`conf`	On Context-Aware DDoS Attacks Using Deep Generative Networks. ICCCN Gonca Gürsun, Murat Sensoy, Melih Kandemir
2018	J	`jnl`	Sampling-Free Variational Inference of Bayesian Neural Nets. CoRR Melih Kandemir, Manuel Haußmann, Fred A. Hamprecht
2018	J	`jnl`	Supervising topic models with Gaussian processes. Pattern Recognit. Melih Kandemir, Taygun Kekeç, Reyyan Yeniterzi
2018	J	`jnl`	Variational closed-Form deep neural net inference. Pattern Recognit. Lett. Melih Kandemir
2017	—	`conf`	Prediction of active UE number with Bayesian neural networks for self-organizing LTE networks. NOF Omer Narmanlioglu, Engin Zeydan, Melih Kandemir, Yakup Tarik Kranda
2017	A*	`conf`	Variational Bayesian Multiple Instance Learning with Gaussian Processes. CVPR Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir
2016	—	`conf`	Gaussian Process Density Counting from Weak Supervision. ECCV (1) Matthias von Borstel, Melih Kandemir, Philip Schmidt, Madhavi K. Rao, Kumar T. Rajamani, Fred A. Hamprecht
2016	A	`conf`	Variational Weakly Supervised Gaussian Processes. BMVC Melih Kandemir, Manuel Haußmann, Ferran Diego, Kumar T. Rajamani, Jeroen van der Laak, Fred A. Hamprecht
2015	A*	`conf`	Asymmetric Transfer Learning with Deep Gaussian Processes. ICML Melih Kandemir
2015	—	`conf`	Cell Event Detection in Phase-Contrast Microscopy Sequences from Few Annotations. MICCAI (3) Melih Kandemir, Christian Wojek, Fred A. Hamprecht
2015	J	`jnl`	Computer-aided diagnosis from weak supervision: A benchmarking study. Comput. Medical Imaging Graph. Melih Kandemir, Fred A. Hamprecht
2015	—	`conf`	Detection of Retinopathy of Prematurity using multiple instance learning. ICACCI Priya Rani, Elagiri Ramalingam Rajkumar, Kumar T. Rajamani, Melih Kandemir, Digvijay Singh
2015	—	`conf`	The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors. FE@NIPS Melih Kandemir, Fred A. Hamprecht
2015	J	`jnl`	Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals. NeuroImage Jukka-Pekka Kauppi, Melih Kandemir, Veli-Matti Saarinen, Lotta Hirvenkari, Lauri Parkkonen, Arto Klami, Riitta Hari, Samuel Kaski
2014	—	`conf`	Digital pathology: Multiple instance learning can detect Barrett's cancer. ISBI Melih Kandemir, Annette Feuchtinger, Axel Walch, Fred A. Hamprecht
2014	—	`conf`	Empowering Multiple Instance Histopathology Cancer Diagnosis by Cell Graphs. MICCAI (2) Melih Kandemir, Chong Zhang, Fred A. Hamprecht
2014	—	`conf`	Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures. MICCAI (2) Melih Kandemir, José C. Rubio, Ute Schmidt, Christian Wojek, Johannes Welbl, Björn Ommer, Fred A. Hamprecht
2014	A	`conf`	Instance Label Prediction by Dirichlet Process Multiple Instance Learning. UAI Melih Kandemir, Fred A. Hamprecht
2014	J	`jnl`	Multi-task and multi-view learning of user state. Neurocomputing Melih Kandemir, Akos Vetek, Mehmet Gönen, Arto Klami, Samuel Kaski
2014	B	`conf`	Multiple Instance Learning with Response-Optimized Random Forests. ICPR Christoph N. Straehle, Melih Kandemir, Ullrich Köthe, Fred A. Hamprecht
2013	—	`—`	Learning Mental States from Biosignals. Melih Kandemir
2012	B	`conf`	Learning relevance from natural eye movements in pervasive interfaces. ICMI Melih Kandemir, Samuel Kaski
2012	—	`conf`	Unsupervised Inference of Auditory Attention from Biosensors. ECML/PKDD (2) Melih Kandemir, Arto Klami, Akos Vetek, Samuel Kaski
2011	J	`jnl`	An augmented reality interface to contextual information. Virtual Real. Antti Ajanki, Mark Billinghurst, Hannes Gamper, Toni Järvenpää, Melih Kandemir, Samuel Kaski, Markus Koskela, Mikko Kurimo, Jorma Laaksonen, Kai Puolamäki, Teemu Ruokolainen, Timo Tossavainen
2011	—	`conf`	Multitask Learning Using Regularized Multiple Kernel Learning. ICONIP (2) Mehmet Gönen, Melih Kandemir, Samuel Kaski
2010	J	`jnl`	Automatic segmentation of colon glands using object-graphs. Medical Image Anal. Cigdem Gunduz Demir, Melih Kandemir, Akif Burak Tosun, Cenk Sokmensuer
2010	B	`conf`	Inferring object relevance from gaze in dynamic scenes. ETRA Melih Kandemir, Veli-Matti Saarinen, Samuel Kaski
2009	J	`jnl`	Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection. Pattern Recognit. Akif Burak Tosun, Melih Kandemir, Cenk Sokmensuer, Cigdem Gunduz Demir