The Adaptive Intelligence Lab

Selected Publications

1. 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},
   }

2. 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},
   }

3. 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-},
   }

4. 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},
     publisher = {IEEE},
   }