Publications | Melih Kandemir

2025

arXiv
Weighted Sequential Bayesian Inference for Non-Stationary Linear Contextual Bandits

N. Werge, Y.S. Wu, A. Akgül, and 1 more author

arXiv Preprint, 2025

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We study non-stationary linear contextual bandits through the lens of sequential Bayesian inference. Whereas existing algorithms typically rely on the Weighted Regularized Least-Squares (WRLS) objective, we study Weighted Sequential Bayesian (WSB), which maintains a posterior distribution over the time-varying reward parameters. Our main contribution is a novel concentration inequality for WSB posteriors, which introduces a prior-dependent term that quantifies the influence of initial beliefs. We show that this influence decays over time and derive tractable upper bounds that make the result useful for both analysis and algorithm design. Building on WSB, we introduce three algorithms: WSB-LinUCB, WSB-RandLinUCB, and WSB-LinTS. We establish frequentist regret guarantees: WSB-LinUCB matches the best-known WRLS-based guarantees, while WSB-RandLinUCB and WSB-LinTS improve upon them, all while preserving the computational efficiency of WRLS-based algorithms.
@article{werge2025wsb, title = {Weighted Sequential Bayesian Inference for Non-Stationary Linear Contextual Bandits}, author = {Werge, N. and Wu, Y.S. and Akgül, A. and Kandemir, M.}, journal = {arXiv Preprint}, year = {2025}, url = {https://arxiv.org/abs/2307.03587} }
arXiv
Directional Ensemble Aggregation for Actor-Critics

N. Werge, Y.S. Wu, B. Tasdighi, and 1 more author

arXiv Preprint, 2025

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Off-policy reinforcement learning in continuous control tasks depends critically on accurate Q-value estimates. Conservative aggregation over ensembles, such as taking the minimum, is commonly used to mitigate overestimation bias. However, these static rules are coarse, discard valuable information from the ensemble, and cannot adapt to task-specific needs or different learning regimes. We propose Directional Ensemble Aggregation (DEA), an aggregation method that adaptively combines Q-value estimates in actor-critic frameworks. DEA introduces two fully learnable directional parameters: one that modulates critic-side conservatism and another that guides actor-side policy exploration. Both parameters are learned using ensemble disagreement-weighted Bellman errors, which weight each sample solely by the direction of its Bellman error. This directional learning mechanism allows DEA to adjust conservatism and exploration in a data-driven way, adapting aggregation to both uncertainty levels and the phase of training. We evaluate DEA across continuous control benchmarks and learning regimes - from interactive to sample-efficient - and demonstrate its effectiveness over static ensemble strategies.
@article{werge2025directionalensemble, title = {Directional Ensemble Aggregation for Actor-Critics}, author = {Werge, N. and Wu, Y.S. and Tasdighi, B. and Kandemir, M.}, year = {2025}, journal = {arXiv Preprint}, url = {https://arxiv.org/abs/2507.23501}, }
arXiv
ObjectRL: An Object-Oriented Reinforcement Learning Codebase

G. Baykal, A. Akgül, M. Haussmann, and 4 more authors

arXiv Preprint, 2025

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ObjectRL is an open-source Python codebase for deep reinforcement learning (RL), designed for research-oriented prototyping with minimal programming effort. Unlike existing codebases, ObjectRL is built on Object-Oriented Programming (OOP) principles, providing a clear structure that simplifies the implementation, modification, and evaluation of new algorithms. ObjectRL lowers the entry barrier for deep RL research by organizing best practices into explicit, clearly separated components, making them easier to understand and adapt. Each algorithmic component is a class with attributes that describe key RL concepts and methods that intuitively reflect their interactions. The class hierarchy closely follows common ontological relationships, enabling data encapsulation, inheritance, and polymorphism, which are core features of OOP. We demonstrate the efficiency of ObjectRL’s design through representative use cases that highlight its flexibility and suitability for rapid prototyping. The documentation and source code are available at https://objectrl.readthedocs.io and https://github.com/adinlab/objectrl .
@article{baykal2025objectrl, title = {ObjectRL: An Object-Oriented Reinforcement Learning Codebase}, author = {Baykal, G. and Akg{\"u}l, A. and Haussmann, M. and Tasdighi, B. and Werge, N. and Wu, Y.S. and Kandemir, M.}, year = {2025}, journal = {arXiv Preprint}, url = {https://arxiv.org/abs/2507.03487}, }
arXiv
Deep Actor-Critics with Tight Risk Certificates

B. Tasdighi, M. Haußmann, Y.S. Wu, and 2 more authors

arXiv Preprint, 2025

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After a period of research, deep actor-critic algorithms have reached a level where they influence our everyday lives. They serve as the driving force behind the continual improvement of large language models through user-collected feedback. However, their deployment in physical systems is not yet widely adopted, mainly because no validation scheme that quantifies their risk of malfunction. We demonstrate that it is possible to develop tight risk certificates for deep actor-critic algorithms that predict generalization performance from validation-time observations. Our key insight centers on the effectiveness of minimal evaluation data. Surprisingly, a small feasible of evaluation roll-outs collected from a pretrained policy suffices to produce accurate risk certificates when combined with a simple adaptation of PAC-Bayes theory. Specifically, we adopt a recently introduced recursive PAC-Bayes approach, which splits validation data into portions and recursively builds PAC-Bayes bounds on the excess loss of each portion’s predictor, using the predictor from the previous portion as a data-informed prior. Our empirical results across multiple locomotion tasks and policy expertise levels demonstrate risk certificates that are tight enough to be considered for practical use.
@article{tasdighi2025deepactorcriticstightrisk, title = {Deep Actor-Critics with Tight Risk Certificates}, author = {Tasdighi, B. and Hau{\ss}mann, M. and Wu, Y.S. and A.R., Masegosa. and Kandemir, M.}, year = {2025}, journal = {arXiv Preprint}, url = {https://arxiv.org/abs/2505.19682}, }
TMLR
Improving Plasticity in Non-stationary Reinforcement Learning with Evidential Proximal Policy Optimization

A. Akgül, G. Baykal, M. Haußmann, and 1 more author

Transactions on Machine Learning Research, 2025

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On-policy reinforcement learning algorithms use the most recently learned policy to interact with the environment and update it using the latest gathered trajectories, making them well-suited for adapting to non-stationary environments where dynamics change over time. However, previous studies show that they struggle to maintain plasticity—the ability of neural networks to adjust their synaptic connections—with overfitting identified as the primary cause. To address this, we present the first application of evidential learning in an on-policy reinforcement learning setting: \textitEvidential Proximal Policy Optimization (EPPO). EPPO incorporates all sources of error in the critic network’s approximation—i.e., the baseline function in advantage calculation—by modeling the epistemic and aleatoric uncertainty contributions to the approximation’s total variance. We achieve this by using an evidential neural network, which serves as a regularizer to prevent overfitting. The resulting probabilistic interpretation of the advantage function enables optimistic exploration, thus maintaining the plasticity. Through experiments on non-stationary continuous control tasks, where the environment dynamics change at regular intervals, we demonstrate that EPPO outperforms state-of-the-art on-policy reinforcement learning variants in both task-specific and overall return.
@article{akgul2025improving, title = {Improving Plasticity in Non-stationary Reinforcement Learning with Evidential Proximal Policy Optimization}, author = {Akg{\"u}l, A. and Baykal, G. and Hau{\ss}mann, M. and Kandemir, M.}, year = {2025}, journal = {Transactions on Machine Learning Research}, url = {https://arxiv.org/abs/2503.01468}, }
ECAI
Improving Actor-Critic Training with Steerable Action-Value Approximation Errors

B. Tasdighi, N. Werge, Y.S. Wu, and 1 more author

In European Conference on Artificial Intelligence, 2025

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Off-policy actor-critic algorithms have shown promise in deep reinforcement learning for continuous control tasks. Their success largely stems from leveraging pessimistic state-action value function updates, which effectively address function approximation errors and improve performance. However, such pessimism can lead to under-exploration, constraining the agent’s ability to explore/refine its policies. Conversely, optimism can counteract under-exploration, but it also carries the risk of excessive risk-taking and poor convergence if not properly balanced. Based on these insights, we introduce Utility Soft Actor-Critic (USAC), a novel framework within the actor-critic paradigm that enables independent control over the degree of pessimism/optimism for both the actor and the critic via interpretable parameters. USAC adapts its exploration strategy based on the uncertainty of critics through a utility function that allows us to balance between optimism and pessimism separately. By going beyond binary choices of optimism and pessimism, USAC represents a significant step towards achieving balance within off-policy actor-critic algorithms. Our experiments across various continuous control problems show that the degree of pessimism or optimism depends on the nature of the task. Furthermore, we demonstrate that USAC can outperform state-of-the-art algorithms for appropriately configured pessimism/optimism parameters.
@inproceedings{tasdighi2025improving, title = {Improving Actor-Critic Training with Steerable Action-Value Approximation Errors}, author = {Tasdighi, B. and Werge, N. and Wu, Y.S. and Kandemir, M.}, year = {2025}, booktitle = {European Conference on Artificial Intelligence}, url = {https://arxiv.org/abs/2406.03890}, }
ECAI
Deep Exploration with PAC-Bayes

B. Tasdighi, M. Haussmann, N. Werge, and 2 more authors

In European Conference on Artificial Intelligence, 2025

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Reinforcement learning (RL) for continuous control under delayed rewards is an under-explored problem despite its significance in real life. Many complex skills build on intermediate ones as prerequisites. For instance, a humanoid locomotor has to learn how to stand before it can learn to walk. To cope with delayed reward, a reinforcement learning agent has to perform deep exploration. However, existing deep exploration methods are designed for small discrete action spaces, and their successful generalization to state-of-the-art continuous control remains unproven. We address the deep exploration problem for the first time from a PAC-Bayesian perspective in the context of actor-critic learning. To do this, we quantify the error of the Bellman operator through a PAC-Bayes bound, where a bootstrapped ensemble of critic networks represents the posterior distribution, and their targets serve as a data-informed function-space prior. We derive an objective function from this bound and use it to train the critic ensemble. Each critic trains an individual soft actor network, implemented as a shared trunk and critic-specific heads. The agent performs deep exploration by acting epsilon-softly on a randomly chosen actor head. Our proposed algorithm, named PAC-Bayesian Actor-Critic (PBAC), is the only algorithm to consistently discover delayed rewards on a diverse set of continuous control tasks with varying difficulty.
@inproceedings{tasdighi2025pbac, title = {Deep Exploration with PAC-Bayes}, author = {Tasdighi, B. and Haussmann, M. and Werge, N. and Wu, Y. and Kandemir, M.}, year = {2025}, booktitle = {European Conference on Artificial Intelligence}, url = {https://arxiv.org/abs/2402.03055}, }
NeuCom
Disentanglement with Factor Quantized Variational Autoencoders

G. Baykal, M. Kandemir, and G. Unal

Neurocomputing, 2025

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Disentangled representation learning aims to represent the underlying generative factors of a dataset in a latent representation independently of one another. In our work, we propose a discrete variational autoencoder (VAE) based model where the ground truth information about the generative factors are not provided to the model. We demonstrate the advantages of learning discrete representations over learning continuous representations in facilitating disentanglement. Furthermore, we propose incorporating an inductive bias into the model to further enhance disentanglement. Precisely, we propose scalar quantization of the latent variables in a latent representation with scalar values from a global codebook, and we add a total correlation term to the optimization as an inductive bias. Our method called FactorQVAE is the first method that combines optimization based disentanglement approaches with discrete representation learning, and it outperforms the former disentanglement methods in terms of two disentanglement metrics (DCI and InfoMEC) while improving the reconstruction performance. Our code can be found at https://github.com/ituvisionlab/FactorQVAE.
@article{baykal2025disentanglement, title = {Disentanglement with Factor Quantized Variational Autoencoders}, author = {Baykal, G. and Kandemir, M. and Unal, G.}, year = {2025}, journal = {Neurocomputing}, url = {https://arxiv.org/abs/2409.14851}, }

2024

NeurIPS
Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning

A. Akgul, M. Haussmann, and M. Kandemir

In Neural Information Processing Systems, 2024

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Current approaches to model-based offline Reinforcement Learning (RL) often incorporate uncertainty-based reward penalization to address the distributional shift problem. While these approaches have achieved some success, we argue that this penalization introduces excessive conservatism, potentially resulting in suboptimal policies through underestimation. We identify as an important cause of over-penalization the lack of a reliable uncertainty estimator capable of propagating uncertainties in the Bellman operator. The common approach to calculating the penalty term relies on sampling-based uncertainty estimation, resulting in high variance. To address this challenge, we propose a novel method termed Moment Matching Offline Model-Based Policy Optimization (MOMBO). MOMBO learns a Q-function using moment matching, which allows us to deterministically propagate uncertainties through the Q-function. We evaluate MOMBO’s performance across various environments and demonstrate empirically that MOMBO is a more stable and sample-efficient approach.
@inproceedings{akgul2024, author = {Akgul, A. and Haussmann, M. and Kandemir, M.}, year = {2024}, title = {Deterministic Uncertainty Propagation for Improved Model-Based Offline Reinforcement Learning}, booktitle = {Neural Information Processing Systems}, url = {https://arxiv.org/abs/2406.04088}, }
L4DC
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

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We study the problem of fitting a model to a dynamical environment when new modes of behavior emerge sequentially. The learning model is aware when a new mode appears, but it does not have access to the true modes of individual training sequences. We devise a novel continual learning method that maintains a descriptor of the mode of an encountered sequence in a neural episodic memory. We employ a Dirichlet Process prior on the attention weights of the memory to foster efficient storage of the mode descriptors. Our method performs continual learning by transferring knowledge across tasks by retrieving the descriptors of similar modes of past tasks to the mode of a current sequence and feeding this descriptor into its transition kernel as control input. We observe the continual learning performance of our method to compare favorably to the mainstream parameter transfer approach.
@inproceedings{akgul2022cddp, title = {Continual Learning of Multi-modal Dynamics with External Memory}, author = {Akgül, A. and Unal, G. and Kandemir, M.}, booktitle = {Learning for Dynamics and Control Conference (L4DC)}, year = {2024}, url = {https://arxiv.org/abs/2203.00936}, }
ICLR
Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting

B. Asan, A. Akgül, A. Unal, and 2 more authors

In Workshop on Tackling Climate Change with Machine Learning, 2024

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Seasonal forecasting is a crucial task when it comes to detecting the extreme heat and colds that occur due to climate change. Confidence in the predictions should be reliable since a small increase in the temperatures in a year has a big impact on the world. Calibration of the neural networks provides a way to ensure our confidence in the predictions. However, calibrating regression models is an under-researched topic, especially in forecasters. We calibrate a UNet++ based architecture, which was shown to outperform physics-based models in temperature anomalies. We show that with a slight trade-off between prediction error and calibration error, it is possible to get more reliable and sharper forecasts. We believe that calibration should be an important part of safety-critical machine learning applications such as weather forecasters.
@inproceedings{asan2024calibrating, title = {Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting}, author = {Asan, B. and Akgül, A. and Unal, A. and Kandemir, M. and Unal, G.}, booktitle = {Workshop on Tackling Climate Change with Machine Learning}, year = {2024}, url = {https://arxiv.org/abs/2403.16612}, }
PatRec
EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoders

G. Baykal, M. Kandemir, and G. Unal

Pattern Recognition, 2024

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Codebook collapse is a common problem in training deep generative models with discrete representation spaces like Vector Quantized Variational Autoencoders (VQ-VAEs). We observe that the same problem arises for the alternatively designed discrete variational autoencoders (dVAEs) whose encoder directly learns a distribution over the codebook embeddings to represent the data. We hypothesize that using the softmax function to obtain a probability distribution causes the codebook collapse by assigning overconfident probabilities to the best matching codebook elements. In this paper, we propose a novel way to incorporate evidential deep learning (EDL) instead of softmax to combat the codebook collapse problem of dVAE. We evidentially monitor the significance of attaining the probability distribution over the codebook embeddings, in contrast to softmax usage. Our experiments using various datasets show that our model, called EdVAE, mitigates codebook collapse while improving the reconstruction performance, and enhances the codebook usage compared to dVAE and VQ-VAE based models.
@article{baykal2024edvae, title = {EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoders}, author = {Baykal, G. and Kandemir, M. and Unal, G.}, journal = {Pattern Recognition}, year = {2024}, url = {https://arxiv.org/abs/2310.05718}, }
AABI
PAC-Bayesian Soft Actor-Critic Learning

B. Tasdighi, A. Akgül, K.K. Brink, and 1 more author

In Advances in Approximate Bayesian Inference Symposium, 2024

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Actor-critic algorithms address the dual goals of reinforcement learning, policy evaluation and improvement, via two separate function approximators. The practicality of this approach comes at the expense of training instability, caused mainly by the destructive effect of the approximation errors of the critic on the actor. We tackle this bottleneck by employing an existing Probably Approximately Correct (PAC) Bayesian bound for the first time as the critic training objective of the Soft Actor-Critic (SAC) algorithm. We further demonstrate that the online learning performance improves significantly when a stochastic actor explores multiple futures by critic-guided random search. We observe our resulting algorithm to compare favorably to the state of the art on multiple classical control and locomotion tasks in both sample efficiency and asymptotic performance.
@inproceedings{bahareh2024pac4sac, title = {PAC-Bayesian Soft Actor-Critic Learning}, author = {Tasdighi, B. and Akgül, A. and Brink, K.K. and Kandemir, M.}, booktitle = {Advances in Approximate Bayesian Inference Symposium}, year = {2024}, url = {https://arxiv.org/abs/2301.12776}, }

2023

NeurIPS
Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures

H. Flynn, D. Reeb, M. Kandemir, and 1 more author

In Neural Information Processing Systems, 2023

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We present improved algorithms with worst-case regret guarantees for the stochastic linear bandit problem. The widely used "optimism in the face of uncertainty" principle reduces a stochastic bandit problem to the construction of a confidence sequence for the unknown reward function. The performance of the resulting bandit algorithm depends on the size of the confidence sequence, with smaller confidence sets yielding better empirical performance and stronger regret guarantees. In this work, we use a novel tail bound for adaptive martingale mixtures to construct confidence sequences which are suitable for stochastic bandits. These confidence sequences allow for efficient action selection via convex programming. We prove that a linear bandit algorithm based on our confidence sequences is guaranteed to achieve competitive worst-case regret. We show that our confidence sequences are tighter than competitors, both empirically and theoretically. Finally, we demonstrate that our tighter confidence sequences give improved performance in several hyperparameter tuning tasks.
@inproceedings{flynn2023improved, author = {Flynn, H. and Reeb, D. and Kandemir, M. and Peters, J.}, year = {2023}, title = {Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures}, booktitle = {Neural Information Processing Systems}, url = {https://arxiv.org/abs/2309.14298}, }
TMLR
Meta Continual Learning on Graphs with Experience Replay

A. Unal, A. Akgül, M. Kandemir, and 1 more author

Transactions on Machine Learning Research, 2023

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Continual learning is a machine learning approach where the challenge is that a constructed learning model executes incoming tasks while maintaining its performance over the earlier tasks. In order to address this issue, we devise a technique that combines two uniquely important concepts in machine learning, namely "replay buffer" and "meta learning", aiming to exploit the best of two worlds. In this method, the model weights are initially computed by using the current task dataset. Next, the dataset of the current task is merged with the stored samples from the earlier tasks and the model weights are updated using the combined dataset. This aids in preventing the model weights converging to the optimal parameters of the current task and enables the preservation of information from earlier tasks. We choose to adapt our technique to graph data structure and the task of node classification on graphs. We introduce MetaCLGraph, which outperforms the baseline methods over various graph datasets including Citeseer, Corafull, Arxiv, and Reddit. This method illustrates the potential of combining replay buffer and meta learning in the field of continual learning on graphs.
@article{unal2023meta, title = {Meta Continual Learning on Graphs with Experience Replay}, author = {Unal, A. and Akg{\"u}l, A. and Kandemir, M. and Unal, G.}, journal = {Transactions on Machine Learning Research}, year = {2023}, url = {https://openreview.net/forum?id=8tnrh56P5W}, }
ACML
Estimation of Counterfactual Interventions under Uncertainties

J. Weilbach, S. Gerwinn, M. Kandemir, and 1 more author

In Asian Conference on Machine Learning, 2023

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Counterfactual analysis is intuitively performed by humans on a daily basis eg. "What should I have done differently to get the loan approved?". Such counterfactual questions also steer the formulation of scientific hypotheses. More formally it provides insights about potential improvements of a system by inferring the effects of hypothetical interventions into a past observation of the system’s behaviour which plays a prominent role in a variety of industrial applications. Due to the hypothetical nature of such analysis, counterfactual distributions are inherently ambiguous. This ambiguity is particularly challenging in continuous settings in which a continuum of explanations exist for the same observation. In this paper, we address this problem by following a hierarchical Bayesian approach which explicitly models such uncertainty. In particular, we derive counterfactual distributions for a Bayesian Warped Gaussian Process thereby allowing for non-Gaussian distributions and non-additive noise. We illustrate the properties our approach on a synthetic and on a semi-synthetic example and show its performance when used within an algorithmic recourse downstream task.
@inproceedings{weilbach2023estimation, title = {Estimation of Counterfactual Interventions under Uncertainties}, author = {Weilbach, J. and Gerwinn, S. and Kandemir, M. and Fraenzle, M.}, booktitle = {Asian Conference on Machine Learning}, url = {https://arxiv.org/abs/2309.08332}, year = {2023}, }
T-PAMI
PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison

H. Flynn, D. Reeb, M. Kandemir, and 1 more author

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023

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PAC-Bayes has recently re-emerged as an effective theory with which one can derive principled learning algorithms with tight performance guarantees. However, applications of PAC-Bayes to bandit problems are relatively rare, which is a great misfortune. Many decision-making problems in healthcare, finance and natural sciences can be modelled as bandit problems. In many of these applications, principled algorithms with strong performance guarantees would be very much appreciated. This survey provides an overview of PAC-Bayes performance bounds for bandit problems and an experimental comparison of these bounds. Our experimental comparison has revealed that available PAC-Bayes upper bounds on the cumulative regret are loose, whereas available PAC-Bayes lower bounds on the expected reward can be surprisingly tight. We found that an offline contextual bandit algorithm that learns a policy by optimising a PAC-Bayes bound was able to learn randomised neural network polices with competitive expected reward and non-vacuous performance guarantees.
@article{flyn2022pacbayes, title = {PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison}, author = {Flynn, H. and Reeb, D. and Kandemir, M. and Peters, J.}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, year = {2023}, url = {https://arxiv.org/abs/2211.16110}, }
MDPI
ALReg: Registration of 3D Point Clouds Using Active Learning

Y.H. Sahin, O. Karabacak, M. Kandemir, and 1 more author

MDPI Applied Sciences, 2023

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After the success of deep learning in point cloud segmentation and classification tasks, it has also been adopted as common practice in point cloud registration applications. State-of-the-art point cloud registration methods generally deal with this problem as a regression task to find the underlying rotation and translation between two point clouds. However, given two point clouds, the transformation between them could be calculated using only definitive point subsets from each cloud. Furthermore, training time is still a major problem among the current registration networks, whereas using a selective approach to define the informative point subsets can lead to reduced network training times. To that end, we developed ALReg, an active learning procedure to select a limited subset of point clouds to train the network. Each of the point clouds in the training set is divided into superpoints (small pieces of each cloud) and the training process is started with a small amount of them. By actively selecting new superpoints and including them in the training process, only a prescribed amount of data is used, hence the time needed to converge drastically decreases. We used DeepBBS, FMR, and DCP methods as our baselines to prove our proposed ALReg method. We trained DeepBBS and DCP on the ModelNet40 dataset and FMR on the 7Scenes dataset. Using 25% of the training data for ModelNet and 4% for the 7Scenes, better or similar accuracy scores are obtained in less than 20% of their original training times. The trained models are also tested on the 3DMatch dataset and better results are obtained than the original FMR training procedure.
@article{sahin2023alreg, title = {ALReg: Registration of 3D Point Clouds Using Active Learning}, author = {Sahin, Y.H. and Karabacak, O. and Kandemir, M. and Unal, G.}, journal = {MDPI Applied Sciences}, year = {2023}, url = {https://www.mdpi.com/2076-3417/13/13/7422}, }
TMLR
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems

A. Look, B. Rakitsch, M. Kandemir, and 1 more author

Transactions on Machine Learning Research, 2023

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Graph neural networks are often used to model interacting dynamical systems since they gracefully scale to systems with a varying and high number of agents. While there has been much progress made for deterministic interacting systems, modeling is much more challenging for stochastic systems in which one is interested in obtaining a predictive distribution over future trajectories. Existing methods are either computationally slow since they rely on Monte Carlo sampling or make simplifying assumptions such that the predictive distribution is unimodal. In this work, we present a deep state-space model which employs graph neural networks in order to model the underlying interacting dynamical system. The predictive distribution is multimodal and has the form of a Gaussian mixture model, where the moments of the Gaussian components can be computed via deterministic moment matching rules. Our moment matching scheme can be exploited for sample-free inference leading to more efficient and stable training compared to Monte Carlo alternatives. Furthermore, we propose structured approximations to the covariance matrices of the Gaussian components in order to scale up to systems with many agents. We benchmark our novel framework on two challenging autonomous driving datasets. Both confirm the benefits of our method compared to state-of-the-art methods. We further demonstrate the usefulness of our individual contributions in a carefully designed ablation study and provide a detailed empirical runtime analysis of our proposed covariance approximations.
@article{look2023cheap, title = {Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems}, author = {Look, A. and Rakitsch, B. and Kandemir, M. and Peters, J.}, journal = {Transactions on Machine Learning Research}, year = {2023}, url = {https://openreview.net/forum?id=dqgdBy4Uv5&noteId=xKtcWgwdxX}, }

2022

NeurIPS
Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs

C. Yildiz, M. Kandemir, and B. Rakitsch

In Neural Information Processing Systems, 2022

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We study for the first time uncertainty-aware modeling of continuous-time dynamics of interacting objects. We introduce a new model that decomposes independent dynamics of single objects accurately from their interactions. By employing latent Gaussian process ordinary differential equations, our model infers both independent dynamics and their interactions with reliable uncertainty estimates. In our formulation, each object is represented as a graph node and interactions are modeled by accumulating the messages coming from neighboring objects. We show that efficient inference of such a complex network of variables is possible with modern variational sparse Gaussian process inference techniques. We empirically demonstrate that our model improves the reliability of long-term predictions over neural network based alternatives and it successfully handles missing dynamic or static information. Furthermore, we observe that only our model can successfully encapsulate independent dynamics and interaction information in distinct functions and show the benefit from this disentanglement in extrapolation scenarios.
@inproceedings{yildiz2022learning, title = {Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs}, author = {Yildiz, C. and Kandemir, M. and Rakitsch, B.}, booktitle = {Neural Information Processing Systems}, year = {2022}, url = {https://arxiv.org/abs/2205.11894}, }
ICLR
Evidential Turing Processes

M. Kandemir, A. Akgül, M. Haussmann, and 1 more author

In International Conference on Learning Representations, 2022

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A probabilistic classifier with reliable predictive uncertainties i) fits successfully to the target domain data, ii) provides calibrated class probabilities in difficult regions of the target domain (e.g. class overlap), and iii) accurately identifies queries coming out of the target domain and reject them. We introduce an original combination of Evidential Deep Learning, Neural Processes, and Neural Turing Machines capable of providing all three essential properties mentioned above for total uncertainty quantification. We observe our method on three image classification benchmarks to consistently improve the in-domain uncertainty quantification, out-of-domain detection, and robustness against input perturbations with one single model. Our unified solution delivers an implementation-friendly and computationally efficient recipe for safety clearance and provides intellectual economy to an investigation of algorithmic roots of epistemic awareness in deep neural nets.
@inproceedings{kandemir2022evidential, title = {Evidential Turing Processes}, author = {Kandemir, M. and Akgül, A. and Haussmann, M. and Unal, G.}, booktitle = {International Conference on Learning Representations}, year = {2022}, url = {https://openreview.net/forum?id=84NMXTHYe-}, }
T-PAMI
A Deterministic Approximation to Neural SDEs

A. Look, M. Kandemir, B. Rakitsch, and 1 more author

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022

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Neural Stochastic Differential Equations (NSDEs) model the drift and diffusion functions of a stochastic process as neural networks. While NSDEs are known to make accurate predictions, their uncertainty quantification properties haven been remained unexplored so far. We report the empirical finding that obtaining well-calibrated uncertainty estimations from NSDEs is computationally prohibitive. As a remedy, we develop a computationally affordable deterministic scheme which accurately approximates the transition kernel, when dynamics is governed by a NSDE. Our method introduces a bidimensional moment matching algorithm: vertical along the neural net layers and horizontal along the time direction, which benefits from an original combination of effective approximations. Our deterministic approximation of the transition kernel is applicable to both training and prediction. We observe in multiple experiments that the uncertainty calibration quality of our method can be matched by Monte Carlo sampling only after introducing high computation cost. Thanks to the numerical stability of deterministic training, our method also provides improvement in prediction accuracy.
@article{look2022adeterministic, title = {A Deterministic Approximation to Neural SDEs}, author = {Look, A. and Kandemir, M. and Rakitsch, B. and Peters, J.}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, year = {2022}, url = {https://arxiv.org/abs/2006.08973}, publisher = {IEEE}, }
L4DC
Traversing Time with Multi-Resolution Gaussian Process State-Space Models

K. Longi, J. Lindinger, O. Duennbier, and 3 more authors

In Learning for Dynamics and Control Conference, 2022

Abs Bib HTML

Gaussian Process state-space models capture complex temporal dependencies in a principled manner by placing a Gaussian Process prior on the transition function. These models have a natural interpretation as discretized stochastic differential equations, but inference for long sequences with fast and slow transitions is difficult. Fast transitions need tight discretizations whereas slow transitions require backpropagating the gradients over long subtrajectories. We propose a novel Gaussian process state-space architecture composed of multiple components, each trained on a different resolution, to model effects on different timescales. The combined model allows traversing time on adaptive scales, providing efficient inference for arbitrarily long sequences with complex dynamics. We benchmark our novel method on semi-synthetic data and on an engine modeling task. In both experiments, our approach compares favorably against its state-of-the-art alternatives that operate on a single time-scale only.
@inproceedings{lungi2022gpssm, title = {Traversing Time with Multi-Resolution Gaussian Process State-Space Models}, author = {Longi, K. and Lindinger, J. and Duennbier, O. and Kandemir, M. and Klami, A. and Rakitsch, B}, booktitle = {Learning for Dynamics and Control Conference}, year = {2022}, url = {https://proceedings.mlr.press/v168/longi22a/longi22a.pdf}, }
DMKD
PAC-Bayesian lifelong learning for multi-armed bandits

H. Flynn, D. Reeb, M. Kandemir, and 1 more author

Data Mining and Knowledge Discovery, 2022

Abs Bib HTML

We present a PAC-Bayesian analysis of lifelong learning. In the lifelong learning problem, a sequence of learning tasks is observed one-at-a-time, and the goal is to transfer information acquired from previous tasks to new learning tasks. We consider the case when each learning task is a multi-armed bandit problem. We derive lower bounds on the expected average reward that would be obtained if a given multi-armed bandit algorithm was run in a new task with a particular prior and for a set number of steps. We propose lifelong learning algorithms that use our new bounds as learning objectives. Our proposed algorithms are evaluated in several lifelong multi-armed bandit problems and are found to perform better than a baseline method that does not use generalisation bounds.
@article{flynn2022pac, title = {PAC-Bayesian lifelong learning for multi-armed bandits}, author = {Flynn, H. and Reeb, D. and Kandemir, M. and Peters, J.}, journal = {Data Mining and Knowledge Discovery}, year = {2022}, publisher = {Springer}, url = {https://link.springer.com/article/10.1007/s10618-022-00825-4}, }

2021

ICML

Inferring the Structure of Ordinary Differential Equations

J. Weilbach, S. Gerwinn, C. Weilbach, and 1 more author

In ICML Time Series Workshop, 2021

Bib HTML

@inproceedings{weilbach2021inferring,
  title = {Inferring the Structure of Ordinary Differential Equations},
  author = {Weilbach, J. and Gerwinn, S. and Weilbach, C. and Kandemir, M.},
  booktitle = {ICML Time Series Workshop},
  year = {2021},
}

AISTATS

Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes

M. Haussmann, S. Gerwinn, A. Look, and 2 more authors

In International Conference on Artificial Intelligence and Statistics, 2021

Bib HTML

@inproceedings{haussmann2021learning,
  title = {Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes},
  author = {Haussmann, M. and Gerwinn, S. and Look, A. and Rakitsch, B. and Kandemir, M.},
  booktitle = {International Conference on Artificial Intelligence and Statistics},
  year = {2021},
}

2020

NeurIPS

Differentiable Implicit Layers

A. Look, S. Doneva, M. Kandemir, and 2 more authors

In NeurIPS ML for Engineering Workshop, 2020

Bib HTML

@inproceedings{look2020differentiable,
  title = {Differentiable Implicit Layers},
  author = {Look, A. and Doneva, S. and Kandemir, M. and Gemulla, R. and Peters, J.},
  booktitle = {NeurIPS ML for Engineering Workshop},
  year = {2020},
}

AABI

Bayesian Evidential Deep Learning with PAC Regularization

M. Haussmann, S. Gerwinn, and M. Kandemir

In Advances in Approximate Bayesian Inference Symposium, 2020

Bib HTML

@inproceedings{haussmann2020bayesian,
  title = {Bayesian Evidential Deep Learning with PAC Regularization},
  author = {Haussmann, M. and Gerwinn, S. and Kandemir, M.},
  booktitle = {Advances in Approximate Bayesian Inference Symposium},
  year = {2020},
}

2019

IJCAI

Deep Active Learning with Adaptive Acquisition

M. Haußmann, F.A. Hamprecht, and M. Kandemir

In International Joint Conference on Artificial Intelligence, 2019

Bib HTML

@inproceedings{haussmann2019deep,
  title = {Deep Active Learning with Adaptive Acquisition},
  author = {Hau{\ss}mann, M. and Hamprecht, F.A. and Kandemir, M.},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year = {2019},
}

NeurIPS

Differential Bayesian Neural Nets

A. Look and M. Kandemir

In NeurIPS Bayesian Deep Learning Workshop, 2019

Bib HTML

@inproceedings{look2019differential,
  title = {Differential Bayesian Neural Nets},
  author = {Look, A. and Kandemir, M.},
  booktitle = {NeurIPS Bayesian Deep Learning Workshop},
  year = {2019},
}

UAI

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

Bib HTML

@inproceedings{haussmann2019sampling,
  title = {{Sampling-Free Variational Inference of Bayesian Neural Nets with Variance Backpropagation}},
  author = {Haussmann, M. and Hamprecht, F.A. and Kandemir, M.},
  booktitle = {International Conference on Uncertainty in Artificial Intelligence},
  pages = {arXiv--preprint},
  year = {2019},
}

2018

Journal

Variational Closed-Form Deep Neural Net Inference

M. Kandemir

Pattern Recognition Letters, 2018

Bib HTML

@article{kandemir2018variational,
  title = {{Variational Closed-Form Deep Neural Net Inference}},
  author = {Kandemir, M.},
  journal = {Pattern Recognition Letters},
  volume = {112},
  pages = {145--151},
  year = {2018},
  publisher = {North-Holland},
}

NeurIPS

Evidential Deep Learning to Quantify Classification Uncertainty

M. Sensoy, L. Kaplan, and M. Kandemir

In Neural Information Processing Systems, 2018

Bib HTML

@inproceedings{sensoy2018evidential,
  title = {Evidential Deep Learning to Quantify Classification Uncertainty},
  author = {Sensoy, M. and Kaplan, L. and Kandemir, M.},
  booktitle = {Neural Information Processing Systems},
  pages = {3179--3189},
  year = {2018},
}

icccn

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

Bib HTML

@inproceedings{gursun2018context,
  title = {{On Context-Aware DDoS Attacks using Deep Generative Networks}},
  author = {G{\"u}rsun, G. and Sensoy, M. and Kandemir, M.},
  booktitle = {International Conference on Computer Communication and Networks (ICCCN)},
  pages = {1--9},
  year = {2018},
  organization = {IEEE},
}

Journal

Supervising Topic Models with Gaussian Processes

M. Kandemir, T. Kekeç, and R. Yeniterzi

Pattern Recognition, 2018

Bib HTML

@article{kandemir2018supervising,
  title = {{Supervising Topic Models with Gaussian Processes}},
  author = {Kandemir, M. and Keke{\c{c}}, T. and Yeniterzi, R.},
  journal = {Pattern Recognition},
  volume = {77},
  pages = {226--236},
  year = {2018},
}

2017

CVPR

Variational Bayesian Multiple Instance Learning with Gaussian Processes

M. Haußmann, F.A. Hamprecht, and M. Kandemir

In Computer Vision and Pattern Recognition, 2017

Bib HTML

@inproceedings{haussmann2017variational,
  title = {Variational Bayesian Multiple Instance Learning with Gaussian Processes},
  author = {Hau{\ss}mann, M. and Hamprecht, F.A. and Kandemir, M.},
  booktitle = {Computer Vision and Pattern Recognition},
  year = {2017},
}

NoF

Prediction of Active UE Number with Bayesian Neural Networks for Self-Organizing LTE Networks

O. Narmanlioglu, E. Zeydan, M. Kandemir, and 1 more author

In International Conference on the Network of the Future (NOF), 2017

Bib HTML

@inproceedings{narmanlioglu2017prediction,
  title = {Prediction of Active UE Number with Bayesian Neural Networks for Self-Organizing LTE Networks},
  author = {Narmanlioglu, O. and Zeydan, E. and Kandemir, M. and Kranda, T.},
  booktitle = {International Conference on the Network of the Future (NOF)},
  pages = {73--78},
  year = {2017},
  organization = {IEEE},
}

2016

ECCV

Gaussian Process Density Counting from Weak Supervision

M.v. Borstel, M. Kandemir, P. Schmidt, and 3 more authors

In European Conference on Computer Vision, 2016

Bib HTML

@inproceedings{borstel2016gaussian,
  title = {Gaussian Process Density Counting from Weak Supervision},
  author = {Borstel, M.v. and Kandemir, M. and Schmidt, P. and Rao, M.K. and Rajamani, K. and Hamprecht, F.A.},
  booktitle = {European Conference on Computer Vision},
  pages = {365--380},
  year = {2016},
  organization = {Springer, Cham},
}

BMVC

Variational Weakly Supervised Gaussian Processes

M. Kandemir, M. Haußmann, F. Diego, and 3 more authors

In British Machine Vision Conference, 2016

Bib HTML

@inproceedings{kandemir2016variational,
  title = {Variational Weakly Supervised Gaussian Processes},
  author = {Kandemir, M. and Hau{\ss}mann, M. and Diego, F. and Rajamani, K. and Van Der Laak, J. and Hamprecht, F.A.},
  booktitle = {British Machine Vision Conference},
  pages = {71--1},
  year = {2016},
}

Journal

Multiple Instance Learning: Robust Validation on Retinopathy of Prematurity

P. Rani, R. Elagiri, K. Rajamani, and 2 more authors

International Journal of Control Theory and Applicatioms, 2016

Bib HTML

@article{rani2016multiple,
  title = {Multiple Instance Learning: Robust Validation on Retinopathy of Prematurity},
  author = {Rani, P. and Elagiri, R. and Rajamani, K. and Kandemir, M. and Singh, D.},
  journal = {International Journal of Control Theory and Applicatioms},
  volume = {9},
  pages = {451--459},
  year = {2016},
}

2015

journal

Computer-Aided Diagnosis from Weak Supervision: A Benchmarking Study

M. Kandemir and F.A. Hamprecht

Computerized medical imaging and graphics, 2015

Bib HTML

@article{kandemir2015computer,
  title = {Computer-Aided Diagnosis from Weak Supervision: A Benchmarking Study},
  author = {Kandemir, M. and Hamprecht, F.A.},
  journal = {Computerized medical imaging and graphics},
  volume = {42},
  pages = {44--50},
  year = {2015},
}

miccai

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

Bib HTML

@inproceedings{kandemir2015cell,
  title = {Cell Event Detection in Phase-Contrast Microscopy Sequences from Few Annotations},
  author = {Kandemir, M. and Wojek, C. and Hamprecht, F.A.},
  booktitle = {International Conference on Medical Image Computing and Computer Assisted Interventions},
  pages = {316--323},
  year = {2015},
  organization = {Springer},
}

ICACCI

Detection of Retinopathy of Prematurity using Multiple Instance Learning

P. Rani, E. Ramalingam Rajkumar, K. Rajamani, and 2 more authors

In International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2015

Bib HTML

@inproceedings{rani2015detection,
  title = {Detection of Retinopathy of Prematurity using Multiple Instance Learning},
  author = {Rani, P. and Rajkumar, E. Ramalingam and Rajamani, K. and Kandemir, M. and Singh, D.},
  booktitle = {International Conference on Advances in Computing, Communications and Informatics (ICACCI)},
  pages = {2233--2237},
  year = {2015},
  organization = {IEEE},
}

Journal

Towards Brain-Activity-Controlled Information Retrieval: Decoding Image Relevance from MEG Signals

J.P. Kauppi, M. Kandemir, V.M. Saarinen, and 5 more authors

NeuroImage, 2015

Bib HTML

@article{kauppi2015towards,
  title = {Towards Brain-Activity-Controlled Information Retrieval: Decoding Image Relevance from MEG Signals},
  author = {Kauppi, J.P. and Kandemir, M. and Saarinen, V.M. and Hirvenkari, L. and Parkkonen, L. and Klami, A. and Hari, R. and Kaski, S.},
  journal = {NeuroImage},
  volume = {112},
  pages = {288--298},
  year = {2015},
  publisher = {Elsevier},
}

ICML

Asymmetric Transfer Learning with Deep Gaussian Processes

M. Kandemir

In International Conference on Machine Learning, 2015

Bib HTML

@inproceedings{kandemir2015asymmetric,
  title = {Asymmetric Transfer Learning with Deep Gaussian Processes},
  author = {Kandemir, M.},
  booktitle = {International Conference on Machine Learning},
  pages = {730--738},
  year = {2015},
  organization = {PMLR},
}

PMLR

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

Bib HTML

@inproceedings{kandemir2015deep,
  title = {The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors},
  author = {Kandemir, M. and Hamprecht, F. A.},
  booktitle = {Feature Extraction: Modern Questions and Challenges},
  year = {2015},
}

2014

MICCAI

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

Bib HTML

@inproceedings{kandemir2014empowering,
  title = {Empowering Multiple Instance Histopathology Cancer Diagnosis by Cell Graphs},
  author = {Kandemir, M. and Zhang, C. and Hamprecht, F.A.},
  booktitle = {International Conference on Medical Image Computing and Computer Assisted Interventions},
  pages = {228--235},
  year = {2014},
  organization = {Springer},
}

ISBI

Digital Pathology: Multiple Instance Learning can Detect Barrett’s Cancer

M. Kandemir, A. Feuchtinger, A. Walch, and 1 more author

In International Symposium on Biomedical Imaging (ISBI), 2014

Bib HTML

@inproceedings{kandemir2014digital,
  title = {Digital Pathology: Multiple Instance Learning can Detect Barrett's Cancer},
  author = {Kandemir, M. and Feuchtinger, A. and Walch, A. and Hamprecht, F.A.},
  booktitle = {International Symposium on Biomedical Imaging (ISBI)},
  pages = {1348--1351},
  year = {2014},
  organization = {IEEE},
}

UAI

Instance Label Prediction by Dirichlet Process Multiple Instance Learning

M. Kandemir and F. A. Hamprecht

In International Conference on Uncertainty in Artificial Intelligence, 2014

Bib HTML

@inproceedings{kandemir2014instance,
  title = {Instance Label Prediction by Dirichlet Process Multiple Instance Learning},
  author = {Kandemir, M. and Hamprecht, F. A.},
  booktitle = {International Conference on Uncertainty in Artificial Intelligence},
  year = {2014},
}

MICCAI

Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures

M. Kandemir, J.C. Rubio, U. Schmidt, and 4 more authors

In International Conference on Medical Image Computing and Computer Assisted Interventions, 2014

Bib HTML

@inproceedings{kandemir2014event,
  title = {Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures},
  author = {Kandemir, M. and Rubio, J.C. and Schmidt, U. and Wojek, C. and Welbl, J. and Ommer, B. and Hamprecht, F.A.},
  booktitle = {International Conference on Medical Image Computing and Computer Assisted Interventions},
  pages = {154--161},
  year = {2014},
  organization = {Springer},
}

Multiple Instance Learning with Response-Optimized Random Forests

C. Straehle, M. Kandemir, U. Koethe, and 1 more author

In International Conference on Pattern Recognition, 2014

Bib HTML

@inproceedings{straehle2014multiple,
  title = {Multiple Instance Learning with Response-Optimized Random Forests},
  author = {Straehle, C. and Kandemir, M. and Koethe, U. and Hamprecht, F.A.},
  booktitle = {International Conference on Pattern Recognition},
  pages = {3768--3773},
  year = {2014},
  organization = {IEEE},
}

Journal

Multi-Task and Multi-View Learning of User State

M. Kandemir, A. Vetek, M. Gönen, and 2 more authors

Neurocomputing, 2014

Bib HTML

@article{kandemir2014multi,
  title = {Multi-Task and Multi-View Learning of User State},
  author = {Kandemir, M. and Vetek, A. and G{\"o}nen, M. and Klami, A. and Kaski, S.},
  journal = {Neurocomputing},
  volume = {139},
  pages = {97--106},
  year = {2014},
  publisher = {Elsevier},
}

2013

Thesis

Learning mental states from biosignals

M. Kandemir

2013

Bib

2012

ICMI

Learning Relevance from Natural Eye Movements in Pervasive Interfaces

M. Kandemir and S. Kaski

In International Conference on Multimodal Interaction, 2012

Bib

@inproceedings{kandemir2012learning,
  title = {Learning Relevance from Natural Eye Movements in Pervasive Interfaces},
  author = {Kandemir, M. and Kaski, S.},
  booktitle = {International Conference on Multimodal Interaction},
  year = {2012},
}

ECML

Unsupervised Inference of Auditory Attention from Biosensors

M. Kandemir, A. Klami, A. Vetek, and 1 more author

In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2012

Bib

@inproceedings{kandemir2012unsupervised,
  title = {Unsupervised Inference of Auditory Attention from Biosensors},
  author = {Kandemir, M. and Klami, A. and Vetek, A. and Kaski, S.},
  booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages = {403--418},
  year = {2012},
  organization = {Springer},
}

2011

ICONIP

Multitask learning using regularized multiple kernel learning

M. Gönen, M. Kandemir, and S. Kaski

In International Conference on Neural Information Processing, 2011

Bib

@inproceedings{gonen2011multitask,
  title = {Multitask learning using regularized multiple kernel learning},
  author = {G{\"o}nen, M. and Kandemir, M. and Kaski, S.},
  booktitle = {International Conference on Neural Information Processing},
  pages = {500--509},
  year = {2011},
  organization = {Springer},
}

journal

An Augmented Reality Interface to Contextual Information

A. Ajanki, M. Billinghurst, H. Gamper, and 8 more authors

Virtual reality, 2011

Bib

@article{ajanki2011augmented,
  title = {An Augmented Reality Interface to Contextual Information},
  author = {Ajanki, A. and Billinghurst, M. and Gamper, H. and J{\"a}rvenp{\"a}{\"a}, T. and Kandemir, M. and Kaski, S. and Koskela, M. and Kurimo, M. and Laaksonen, J. and Puolam{\"a}ki, K. and others},
  journal = {Virtual reality},
  volume = {15},
  number = {2},
  pages = {161--173},
  year = {2011},
  publisher = {Springer-Verlag},
}

2010

MLSP

Contextual information access with augmented reality

A. Ajanki, M. Billinghurst, T. Järvenpää, and 8 more authors

In International Workshop on Machine Learning for Signal Processing, 2010

Bib

@inproceedings{ajanki2010contextual,
  title = {Contextual information access with augmented reality},
  author = {Ajanki, A. and Billinghurst, M. and J{\"a}rvenp{\"a}{\"a}, T. and Kandemir, M. and Kaski, S. and Koskela, M. and Kurimo, M. and Laaksonen, J. and Puolam{\"a}ki, K. and Ruokolainen, T. and others},
  booktitle = {International Workshop on Machine Learning for Signal Processing},
  pages = {95--100},
  year = {2010},
}

Journal

Automatic segmentation of colon glands using object-graphs

C. Gunduz-Demir, M. Kandemir, A.B. Tosun, and 1 more author

Medical image analysis, 2010

Bib

@article{gunduz2010automatic,
  title = {Automatic segmentation of colon glands using object-graphs},
  author = {Gunduz-Demir, C. and Kandemir, M. and Tosun, A.B. and Sokmensuer, C.},
  journal = {Medical image analysis},
  volume = {14},
  number = {1},
  pages = {1--12},
  year = {2010},
  publisher = {Elsevier},
}

ETRA

Inferring Object Relevance from Gaze in Dynamic Scenes

M. Kandemir, V.M. Saarinen, and S. Kaski

In Symposium on Eye-Tracking Research & Applications, 2010

Bib

@inproceedings{kandemir2010inferring,
  title = {Inferring Object Relevance from Gaze in Dynamic Scenes},
  author = {Kandemir, M. and Saarinen, V.M. and Kaski, S.},
  booktitle = {Symposium on Eye-Tracking Research \& Applications},
  pages = {105--108},
  year = {2010},
}

2009

Journal

Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection

A.B. Tosun, M. Kandemir, C. Sokmensuer, and 1 more author

Pattern Recognition, 2009

Bib

@article{tosun2009object,
  title = {Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection},
  author = {Tosun, A.B. and Kandemir, M. and Sokmensuer, C. and Gunduz-Demir, C.},
  journal = {Pattern Recognition},
  volume = {42},
  number = {6},
  pages = {1104--1112},
  year = {2009},
  publisher = {Elsevier},
}

2008

Thesis

Segmentation of Colon Glands by Object Graphs

M. Kandemir

Bilkent University, 2008

Bib

2007

CGI

A Framework for Real-Time Animation of Liquid-Rigid Body Interaction

M. Kandemi̇r, T. Çapin, and B. Özgüç

2007

Bib

@article{kandemir2007framework,
  title = {A Framework for Real-Time Animation of Liquid-Rigid Body Interaction},
  author = {Kandemi̇r, M. and {\c{C}}apin, T. and {\"O}zg{\"u}{\c{c}}, B.},
  booktitle = {Computer Graphics International (CGI)},
  year = {2007},
}