==========
References
==========

Main Papers
===========

.. [Lee2022] Lee, J., Zaheer, M., Sra, S., & Jadbabaie, A. (2022).
   *Online Hyperparameter Meta-Learning with Hypergradient Distillation*.
   ICLR 2022. `arXiv:2110.02508 <https://arxiv.org/abs/2110.02508>`_

.. [Luketina2016] Luketina, J., Berglund, M., Greff, K., & Raiko, T. (2016).
   *Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters*.
   ICML 2016. `arXiv:1511.06727 <https://arxiv.org/abs/1511.06727>`_

.. [Liu2018] Liu, H., Simonyan, K., & Yang, Y. (2018).
   *DARTS: Differentiable Architecture Search*.
   ICLR 2019. `arXiv:1806.09055 <https://arxiv.org/abs/1806.09055>`_

.. [Anonymous2025] Anonymous. (2025).
   *Generalized Greedy Gradient Hyperparameter Optimization*.
   Under review at ICLR 2025.

Related Work
============

.. [Franceschi2017] Franceschi, L., Donini, M., Frasconi, P., & Pontil, M. (2017).
   *Forward and Reverse Gradient-Based Hyperparameter Optimization*.
   ICML 2017. `arXiv:1703.01785 <https://arxiv.org/abs/1703.01785>`_

.. [Baydin2018] Baydin, A. G., Cornish, R., Rubio, D. M., Schmidt, M., & Wood, F. (2018).
   *Online Learning Rate Adaptation with Hypergradient Descent*.
   ICLR 2018. `arXiv:1703.04782 <https://arxiv.org/abs/1703.04782>`_

.. [Nichol2018] Nichol, A., Achiam, J., & Schulman, J. (2018).
   *On First-Order Meta-Learning Algorithms*.
   `arXiv:1803.02999 <https://arxiv.org/abs/1803.02999>`_

.. [Fu2016] Fu, J., Baydin, A. G., & Wood, F. (2016).
   *DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks*.
   `arXiv:1601.00917 <https://arxiv.org/abs/1601.00917>`_

Citation
========

If you use GradHpO in your research, please cite:

   Eynullayev, A., Rubtsov, D., & Karpeev, G. (2026).
   *GradHpO: Gradient-Based Hyperparameter Optimization*.
   MIPT Intelligent Systems.