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ORCIDMelih Kandemir
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- affiliation: University of Southern Denmark, Odense, Denmark
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2020 – today
- 2024
[j14]Gulcin Baykal, Melih Kandemir
, Gozde Unal:
EdVAE: Mitigating codebook collapse with evidential discrete variational autoencoders. Pattern Recognit. 156: 110792 (2024)- [c28]Abdullah Akgül, Gozde Unal, Melih Kandemir:
Continual learning of multi-modal dynamics with external memory. L4DC 2024: 40-51 - [i28]Bahareh Tasdighi, Nicklas Werge, Yi-Shan Wu, Melih Kandemir:
Probabilistic Actor-Critic: Learning to Explore with PAC-Bayes Uncertainty. CoRR abs/2402.03055 (2024) - [i27]Busra Asan, Abdullah Akgül, Alper Unal, Melih Kandemir, Gozde Unal:
Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting. CoRR abs/2403.16612 (2024) - [i26]Bahareh Tasdighi, Nicklas Werge, Yi-Shan Wu, Melih Kandemir:
Exploring Pessimism and Optimism Dynamics in Deep Reinforcement Learning. CoRR abs/2406.03890 (2024) - [i25]Abdullah Akgül, Manuel Haußmann, Melih Kandemir:
Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning. CoRR abs/2406.04088 (2024) - [i24]Gulcin Baykal, Melih Kandemir, Gozde Unal:
Disentanglement with Factor Quantized Variational Autoencoders. CoRR abs/2409.14851 (2024) - 2023
- [j13]Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters:
A Deterministic Approximation to Neural SDEs. IEEE Trans. Pattern Anal. Mach. Intell. 45(4): 4023-4037 (2023) - [j12]Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters:
PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison. IEEE Trans. Pattern Anal. Mach. Intell. 45(12): 15308-15327 (2023) - [j11]Andreas Look, Barbara Rakitsch, Melih Kandemir, Jan Peters:
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems. Trans. Mach. Learn. Res. 2023 (2023) - [j10]Altay Unal, Abdullah Akgül, Melih Kandemir, Gozde Unal:
Meta Continual Learning on Graphs with Experience Replay. Trans. Mach. Learn. Res. 2023 (2023) - [c27]Juliane Weilbach, Sebastian Gerwinn, Melih Kandemir, Martin Fränzle:
Estimation of Counterfactual Interventions under Uncertainties. ACML 2023: 1463-1478 - [c26]Hamish Flynn, David Reeb, Melih Kandemir, Jan R. Peters:
Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures. NeurIPS 2023 - [i23]Bahareh Tasdighi, Abdullah Akgül, Kenny Kazimirzak Brink, Melih Kandemir:
PAC-Bayesian Soft Actor-Critic Learning. CoRR abs/2301.12776 (2023) - [i22]Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters:
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems. CoRR abs/2305.01773 (2023) - [i21]Nicklas Werge, Abdullah Akgül, Melih Kandemir:
BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits. CoRR abs/2307.03587 (2023) - [i20]Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters:
Sampling-Free Probabilistic Deep State-Space Models. CoRR abs/2309.08256 (2023) - [i19]Juliane Weilbach, Sebastian Gerwinn, Melih Kandemir, Martin Fränzle:
Estimation of Counterfactual Interventions under Uncertainties. CoRR abs/2309.08332 (2023) - [i18]Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters:
Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures. CoRR abs/2309.14298 (2023) - [i17]Gulcin Baykal, Melih Kandemir, Gozde Unal:
EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoders. CoRR abs/2310.05718 (2023) - [i16]Aritra Dutta, El Houcine Bergou, Soumia Boucherouite, Nicklas Werge, Melih Kandemir, Xin Li:
Demystifying the Myths and Legends of Nonconvex Convergence of SGD. CoRR abs/2310.12969 (2023) - 2022
- [j9]Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters:
PAC-Bayesian lifelong learning for multi-armed bandits. Data Min. Knowl. Discov. 36(2): 841-876 (2022) - [c25]Melih Kandemir, Abdullah Akgül, Manuel Haußmann, Gozde Unal:
Evidential Turing Processes. ICLR 2022 - [c24]Krista Longi, Jakob Lindinger, Olaf Duennbier, Melih Kandemir, Arto Klami, Barbara Rakitsch:
Traversing Time with Multi-Resolution Gaussian Process State-Space Models. L4DC 2022: 366-377 - [c23]Çagatay Yildiz, Melih Kandemir, Barbara Rakitsch:
Learning interacting dynamical systems with latent Gaussian process ODEs. NeurIPS 2022 - [i15]Abdullah Akgül, Gozde Unal, Melih Kandemir:
Continual Learning of Multi-modal Dynamics with External Memory. CoRR abs/2203.00936 (2022) - [i14]Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters:
PAC-Bayesian Lifelong Learning For Multi-Armed Bandits. CoRR abs/2203.03303 (2022) - [i13]Çagatay Yildiz, Melih Kandemir, Barbara Rakitsch:
Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs. CoRR abs/2205.11894 (2022) - [i12]Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters:
PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison. CoRR abs/2211.16110 (2022) - 2021
- [c22]Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir:
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. AISTATS 2021: 478-486 - [i11]Melih Kandemir, Abdullah Akgül, Manuel Haußmann, Gozde Unal:
Evidential Turing Processes. CoRR abs/2106.01216 (2021) - [i10]Juliane Weilbach, Sebastian Gerwinn, Christian Weilbach, Melih Kandemir:
Inferring the Structure of Ordinary Differential Equations. CoRR abs/2107.07345 (2021) - [i9]Krista Longi, Jakob Lindinger, Olaf Duennbier, Melih Kandemir, Arto Klami, Barbara Rakitsch:
Traversing Time with Multi-Resolution Gaussian Process State-Space Models. CoRR abs/2112.03230 (2021) - 2020
- [i8]Andreas Look, Chen Qiu, Maja Rudolph, Jan Peters, Melih Kandemir:
Deterministic Inference of Neural Stochastic Differential Equations. CoRR abs/2006.08973 (2020) - [i7]Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir:
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. CoRR abs/2006.09914 (2020) - [i6]Andreas Look, Simona Doneva, Melih Kandemir, Rainer Gemulla, Jan Peters:
Differentiable Implicit Layers. CoRR abs/2010.07078 (2020)
2010 – 2019
- 2019
- [c21]Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir:
Deep Active Learning with Adaptive Acquisition. IJCAI 2019: 2470-2476 - [c20]Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir:
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation. UAI 2019: 563-573 - [i5]Manuel Haußmann, Sebastian Gerwinn, Melih Kandemir:
Bayesian Prior Networks with PAC Training. CoRR abs/1906.00816 (2019) - [i4]Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir:
Deep Active Learning with Adaptive Acquisition. CoRR abs/1906.11471 (2019) - [i3]Andreas Look, Melih Kandemir:
Differential Bayesian Neural Nets. CoRR abs/1912.00796 (2019) - 2018
- [j8]Melih Kandemir, Taygun Kekeç, Reyyan Yeniterzi:
Supervising topic models with Gaussian processes. Pattern Recognit. 77: 226-236 (2018) - [j7]Melih Kandemir:
Variational closed-Form deep neural net inference. Pattern Recognit. Lett. 112: 145-151 (2018) - [c19]Gonca Gürsun, Murat Sensoy, Melih Kandemir:
On Context-Aware DDoS Attacks Using Deep Generative Networks. ICCCN 2018: 1-9 - [c18]Murat Sensoy, Lance M. Kaplan, Melih Kandemir:
Evidential Deep Learning to Quantify Classification Uncertainty. NeurIPS 2018: 3183-3193 - [i2]Melih Kandemir, Manuel Haußmann, Fred A. Hamprecht:
Sampling-Free Variational Inference of Bayesian Neural Nets. CoRR abs/1805.07654 (2018) - [i1]Murat Sensoy, Melih Kandemir, Lance M. Kaplan:
Evidential Deep Learning to Quantify Classification Uncertainty. CoRR abs/1806.01768 (2018) - 2017
- [c17]Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir:
Variational Bayesian Multiple Instance Learning with Gaussian Processes. CVPR 2017: 810-819 - [c16]Omer Narmanlioglu, Engin Zeydan, Melih Kandemir, Yakup Tarik Kranda:
Prediction of active UE number with Bayesian neural networks for self-organizing LTE networks. NOF 2017: 73-78 - 2016
- [c15]Melih Kandemir, Manuel Haußmann, Ferran Diego, Kumar T. Rajamani, Jeroen van der Laak, Fred A. Hamprecht:
Variational Weakly Supervised Gaussian Processes. BMVC 2016 - [c14]Matthias von Borstel, Melih Kandemir, Philip Schmidt, Madhavi K. Rao, Kumar T. Rajamani, Fred A. Hamprecht:
Gaussian Process Density Counting from Weak Supervision. ECCV (1) 2016: 365-380 - 2015
- [j6]Melih Kandemir, Fred A. Hamprecht:
Computer-aided diagnosis from weak supervision: A benchmarking study. Comput. Medical Imaging Graph. 42: 44-50 (2015) - [j5]Jukka-Pekka Kauppi, Melih Kandemir, Veli-Matti Saarinen, Lotta Hirvenkari, Lauri Parkkonen, Arto Klami, Riitta Hari, Samuel Kaski:
Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals. NeuroImage 112: 288-298 (2015) - [c13]Priya Rani, Elagiri Ramalingam Rajkumar, Kumar T. Rajamani, Melih Kandemir, Digvijay Singh:
Detection of Retinopathy of Prematurity using multiple instance learning. ICACCI 2015: 2233-2237 - [c12]Melih Kandemir:
Asymmetric Transfer Learning with Deep Gaussian Processes. ICML 2015: 730-738 - [c11]Melih Kandemir, Christian Wojek, Fred A. Hamprecht:
Cell Event Detection in Phase-Contrast Microscopy Sequences from Few Annotations. MICCAI (3) 2015: 316-323 - [c10]Melih Kandemir, Fred A. Hamprecht:
The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors. FE@NIPS 2015: 145-159 - 2014
- [j4]Melih Kandemir, Akos Vetek, Mehmet Gönen, Arto Klami, Samuel Kaski:
Multi-task and multi-view learning of user state. Neurocomputing 139: 97-106 (2014) - [c9]Christoph N. Straehle, Melih Kandemir, Ullrich Köthe, Fred A. Hamprecht:
Multiple Instance Learning with Response-Optimized Random Forests. ICPR 2014: 3768-3773 - [c8]Melih Kandemir, Annette Feuchtinger, Axel Walch, Fred A. Hamprecht:
Digital pathology: Multiple instance learning can detect Barrett's cancer. ISBI 2014: 1348-1351 - [c7]Melih Kandemir, José C. Rubio, Ute Schmidt, Christian Wojek, Johannes Welbl, Björn Ommer, Fred A. Hamprecht:
Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures. MICCAI (2) 2014: 154-161 - [c6]Melih Kandemir, Chong Zhang, Fred A. Hamprecht:
Empowering Multiple Instance Histopathology Cancer Diagnosis by Cell Graphs. MICCAI (2) 2014: 228-235 - [c5]Melih Kandemir, Fred A. Hamprecht:
Instance Label Prediction by Dirichlet Process Multiple Instance Learning. UAI 2014: 380-389 - 2013
- [b1]Melih Kandemir:
Learning Mental States from Biosignals. Aalto University, Helsinki, Finland, 2013 - 2012
- [c4]Melih Kandemir, Samuel Kaski:
Learning relevance from natural eye movements in pervasive interfaces. ICMI 2012: 85-92 - [c3]Melih Kandemir, Arto Klami, Akos Vetek, Samuel Kaski:
Unsupervised Inference of Auditory Attention from Biosensors. ECML/PKDD (2) 2012: 403-418 - 2011
- [j3]Antti Ajanki, Mark Billinghurst, Hannes Gamper, Toni Järvenpää, Melih Kandemir, Samuel Kaski, Markus Koskela, Mikko Kurimo, Jorma Laaksonen, Kai Puolamäki, Teemu Ruokolainen, Timo Tossavainen:
An augmented reality interface to contextual information. Virtual Real. 15(2-3): 161-173 (2011) - [c2]Mehmet Gönen, Melih Kandemir, Samuel Kaski:
Multitask Learning Using Regularized Multiple Kernel Learning. ICONIP (2) 2011: 500-509 - 2010
- [j2]Cigdem Gunduz Demir, Melih Kandemir, Akif Burak Tosun, Cenk Sokmensuer:
Automatic segmentation of colon glands using object-graphs. Medical Image Anal. 14(1): 1-12 (2010) - [c1]Melih Kandemir, Veli-Matti Saarinen, Samuel Kaski:
Inferring object relevance from gaze in dynamic scenes. ETRA 2010: 105-108
2000 – 2009
- 2009
- [j1]Akif Burak Tosun, Melih Kandemir, Cenk Sokmensuer, Cigdem Gunduz Demir:
Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection. Pattern Recognit. 42(6): 1104-1112 (2009)
Coauthor Index
aka: Jan R. Peters
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