Melih Kandemir

ORCID

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 50

Top Venues

Aalto University, Helsinki, Finland (PHD)

Education History

Aalto University, Helsinki, Finland

PHD

Publications

Bahareh Tasdighi, Nicklas Werge, Yi-Shan Wu, Melih Kandemir
Exploring Pessimism and Optimism Dynamics in Deep Reinforcement Learning. CoRR (2024)
Abdullah Akgül, Gozde Unal, Melih Kandemir
Continual learning of multi-modal dynamics with external memory. L4DC (2024)
Bahareh Tasdighi, Nicklas Werge, Yi-Shan Wu, Melih Kandemir
Probabilistic Actor-Critic: Learning to Explore with PAC-Bayes Uncertainty. CoRR (2024)
Gulcin Baykal, Melih Kandemir, Gozde Unal
EdVAE: Mitigating codebook collapse with evidential discrete variational autoencoders. Pattern Recognit. 156 (2024)
Abdullah Akgül, Manuel Haußmann, Melih Kandemir
Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning. CoRR (2024)
Busra Asan, Abdullah Akgül, Alper Unal, Melih Kandemir, Gozde Unal
Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting. CoRR (2024)
Hamish Flynn, David Reeb, Melih Kandemir, Jan R. Peters
Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures. NeurIPS (2023)
Bahareh Tasdighi, Abdullah Akgül, Kenny Kazimirzak Brink, Melih Kandemir
PAC-Bayesian Soft Actor-Critic Learning. CoRR (2023)
Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters
Sampling-Free Probabilistic Deep State-Space Models. CoRR (2023)
Juliane Weilbach, Sebastian Gerwinn, Melih Kandemir, Martin Fränzle
Estimation of Counterfactual Interventions under Uncertainties. CoRR (2023)
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) (2023)
Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters
A Deterministic Approximation to Neural SDEs. IEEE Trans. Pattern Anal. Mach. Intell. 45 (4) (2023)
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)
Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters
Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures. CoRR (2023)
Aritra Dutta, El Houcine Bergou, Soumia Boucherouite, Nicklas Werge, Melih Kandemir, Xin Li
Demystifying the Myths and Legends of Nonconvex Convergence of SGD. CoRR (2023)
Gulcin Baykal, Melih Kandemir, Gozde Unal
EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoders. CoRR (2023)
Juliane Weilbach, Sebastian Gerwinn, Melih Kandemir, Martin Fränzle
Estimation of Counterfactual Interventions under Uncertainties. ACML (2023)
Altay Unal, Abdullah Akgül, Melih Kandemir, Gozde Unal
Meta Continual Learning on Graphs with Experience Replay. Trans. Mach. Learn. Res. 2023 (2023)
Andreas Look, Melih Kandemir, Barbara Rakitsch, Jan Peters
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems. CoRR (2023)
Nicklas Werge, Abdullah Akgül, Melih Kandemir
BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits. CoRR (2023)
Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters
PAC-Bayesian lifelong learning for multi-armed bandits. Data Min. Knowl. Discov. 36 (2) (2022)
Krista Longi, Jakob Lindinger, Olaf Duennbier, Melih Kandemir, Arto Klami, Barbara Rakitsch
Traversing Time with Multi-Resolution Gaussian Process State-Space Models. L4DC (2022)
Çagatay Yildiz, Melih Kandemir, Barbara Rakitsch
Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs. CoRR (2022)
Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters
PAC-Bayesian Lifelong Learning For Multi-Armed Bandits. CoRR (2022)
Melih Kandemir, Abdullah Akgül, Manuel Haußmann, Gozde Unal
Evidential Turing Processes. ICLR (2022)
Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters
PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison. CoRR (2022)
Abdullah Akgül, Gozde Unal, Melih Kandemir
Continual Learning of Multi-modal Dynamics with External Memory. CoRR (2022)
Çagatay Yildiz, Melih Kandemir, Barbara Rakitsch
Learning interacting dynamical systems with latent Gaussian process ODEs. NeurIPS (2022)
Melih Kandemir, Abdullah Akgül, Manuel Haußmann, Gozde Unal
Evidential Turing Processes. CoRR (2021)
Juliane Weilbach, Sebastian Gerwinn, Christian Weilbach, Melih Kandemir
Inferring the Structure of Ordinary Differential Equations. CoRR (2021)
Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. AISTATS (2021)
Krista Longi, Jakob Lindinger, Olaf Duennbier, Melih Kandemir, Arto Klami, Barbara Rakitsch
Traversing Time with Multi-Resolution Gaussian Process State-Space Models. CoRR (2021)
Manuel Haußmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. CoRR (2020)
Andreas Look, Simona Doneva, Melih Kandemir, Rainer Gemulla, Jan Peters
Differentiable Implicit Layers. CoRR (2020)
Andreas Look, Chen Qiu, Maja Rudolph, Jan Peters, Melih Kandemir
Deterministic Inference of Neural Stochastic Differential Equations. CoRR (2020)
Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation. UAI (2019)
Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir
Deep Active Learning with Adaptive Acquisition. IJCAI (2019)
Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir
Deep Active Learning with Adaptive Acquisition. CoRR (2019)
Manuel Haußmann, Sebastian Gerwinn, Melih Kandemir
Bayesian Prior Networks with PAC Training. CoRR (2019)
Andreas Look, Melih Kandemir
Differential Bayesian Neural Nets. CoRR (2019)
Murat Sensoy, Melih Kandemir, Lance M. Kaplan
Evidential Deep Learning to Quantify Classification Uncertainty. CoRR (2018)
Melih Kandemir
Variational closed-Form deep neural net inference. Pattern Recognit. Lett. 112 (2018)
Gonca Gürsun, Murat Sensoy, Melih Kandemir
On Context-Aware DDoS Attacks Using Deep Generative Networks. ICCCN (2018)
Murat Sensoy, Lance M. Kaplan, Melih Kandemir
Evidential Deep Learning to Quantify Classification Uncertainty. NeurIPS (2018)
Melih Kandemir, Taygun Kekeç, Reyyan Yeniterzi
Supervising topic models with Gaussian processes. Pattern Recognit. 77 (2018)
Melih Kandemir, Manuel Haußmann, Fred A. Hamprecht
Sampling-Free Variational Inference of Bayesian Neural Nets. CoRR (2018)
Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir
Variational Bayesian Multiple Instance Learning with Gaussian Processes. CVPR (2017)
Omer Narmanlioglu, Engin Zeydan, Melih Kandemir, Tarik Kranda
Prediction of active UE number with Bayesian neural networks for self-organizing LTE networks. NOF (2017)
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)
Melih Kandemir, Manuel Haußmann, Ferran Diego, Kumar T. Rajamani, Jeroen van der Laak, Fred A. Hamprecht
Variational Weakly Supervised Gaussian Processes. BMVC (2016)
Melih Kandemir, Christian Wojek, Fred A. Hamprecht
Cell Event Detection in Phase-Contrast Microscopy Sequences from Few Annotations. MICCAI (3) (2015)
Melih Kandemir, Fred A. Hamprecht
Computer-aided diagnosis from weak supervision: A benchmarking study. Comput. Medical Imaging Graph. 42 (2015)
Priya Rani, Elagiri Ramalingam Rajkumar, Kumar T. Rajamani, Melih Kandemir, Digvijay Singh
Detection of Retinopathy of Prematurity using multiple instance learning. ICACCI (2015)
Melih Kandemir
Asymmetric Transfer Learning with Deep Gaussian Processes. ICML (2015)
Melih Kandemir, Fred A. Hamprecht
The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors. FE@NIPS (2015)
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 (2015)
Melih Kandemir, Annette Feuchtinger, Axel Walch, Fred A. Hamprecht
Digital pathology: Multiple instance learning can detect Barrett's cancer. ISBI (2014)
Melih Kandemir, Fred A. Hamprecht
Instance Label Prediction by Dirichlet Process Multiple Instance Learning. UAI (2014)
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)
Melih Kandemir, Akos Vetek, Mehmet Gönen, Arto Klami, Samuel Kaski
Multi-task and multi-view learning of user state. Neurocomputing 139 (2014)
Melih Kandemir, Chong Zhang, Fred A. Hamprecht
Empowering Multiple Instance Histopathology Cancer Diagnosis by Cell Graphs. MICCAI (2) (2014)
Christoph N. Straehle, Melih Kandemir, Ullrich Köthe, Fred A. Hamprecht
Multiple Instance Learning with Response-Optimized Random Forests. ICPR (2014)
Melih Kandemir, Samuel Kaski
Learning relevance from natural eye movements in pervasive interfaces. ICMI (2012)
Melih Kandemir, Arto Klami, Akos Vetek, Samuel Kaski
Unsupervised Inference of Auditory Attention from Biosensors. ECML/PKDD (2) (2012)
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) (2011)
Mehmet Gönen, Melih Kandemir, Samuel Kaski
Multitask Learning Using Regularized Multiple Kernel Learning. ICONIP (2) (2011)
Cigdem Gunduz Demir, Melih Kandemir, Akif Burak Tosun, Cenk Sokmensuer
Automatic segmentation of colon glands using object-graphs. Medical Image Anal. 14 (1) (2010)
Melih Kandemir, Veli-Matti Saarinen, Samuel Kaski
Inferring object relevance from gaze in dynamic scenes. ETRA (2010)
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) (2009)

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Publication Activity (10 Years)

Top Topics

Top Venues

Melih Kandemir