I am a PhD student in Computer Science at MIT EECS & MIT CSAIL,
where I am fortunate to be advised by professor Tommi Jaakkola.
My research is focused on probabilistic machine learning and deep learning. I am interested in generative models, empirical approaches to understanding training, robustness, and generalization of deep neural networks.
In the summer of 2021, I completed a research internship at Google Brain, working with Chiyuan Zhang.
In the summer of 2023, I completed an internship at Cruise AI Research team supervised by David Hayden.
@article{garipov2023compositional,
title={Compositional Sculpting of Iterative Generative Processes},
author={Garipov, Timur and De Peuter, Sebastiaan and Yang, Ge and Garg, Vikas and Kaski, Samuel and Jaakkola, Tommi},
journal={Advances in Neural Information Processing Systems},
volume={36},
year={2023}
}
Adversarial Support Alignment
Shangyuan Tong, Timur Garipov, Yang Zhang, Shiyu Chang, and Tommi Jaakkola
ICLR 2022: International Conference on Learning Representations
Spotlight Presentation
@inproceedings{tong2022adversarial,
title={Adversarial Support Alignment},
author={Shangyuan Tong and Timur Garipov and Yang Zhang and Shiyu Chang and Tommi S. Jaakkola},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=26gKg6x-ie}
}
Subspace inference for Bayesian deep learning
Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson
@inproceedings{izmailov2020subspace,
title={Subspace inference for Bayesian deep learning},
author={Izmailov, Pavel and Maddox, Wesley J and Kirichenko, Polina and Garipov, Timur and Vetrov, Dmitry and Wilson, Andrew Gordon},
booktitle={Uncertainty in Artificial Intelligence},
pages={1169--1179},
year={2020},
organization={PMLR}
}
A Simple Baseline for Bayesian Uncertainty in Deep Learning
Wesley J. Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, and Andrew Gordon Wilson
NeurIPS 2019: Neural Information Processing Systems
@article{maddox2019simple,
title={A simple baseline for bayesian uncertainty in deep learning},
author={Maddox, Wesley J and Izmailov, Pavel and Garipov, Timur and Vetrov, Dmitry P and Wilson, Andrew Gordon},
journal={Advances in Neural Information Processing Systems},
volume={32},
year={2019}
}
Averaging Weights Leads to Wider Optima and Better Generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson
UAI 2018: Uncertainty in Artificial Intelligence
Oral Presentation
@article{izmailov2018averaging,
title={Averaging weights leads to wider optima and better generalization},
author={Izmailov, Pavel and Podoprikhin, Dmitrii and Garipov, Timur and Vetrov, Dmitry and Wilson, Andrew Gordon},
journal={Uncertainty in Artificial Intelligence (UAI)},
year={2018}
}
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry Vetrov, and Andrew Gordon Wilson
NeurIPS 2018: Neural Information Processing Systems
Spotlight Presentation
@article{garipov2018loss,
title={Loss surfaces, mode connectivity, and fast ensembling of dnns},
author={Garipov, Timur and Izmailov, Pavel and Podoprikhin, Dmitrii and Vetrov, Dmitry P and Wilson, Andrew G},
journal={Advances in neural information processing systems},
volume={31},
year={2018}
}
Workshop papers, preprints, other projects
The Benefits of Pairwise Discriminators for Adversarial Training
Shangyuan Tong, Timur Garipov, and Tommi Jaakkola
@article{tong2020benefits,
title={The Benefits of Pairwise Discriminators for Adversarial Training},
author={Tong, Shangyuan and Garipov, Timur and Jaakkola, Tommi},
journal={arXiv preprint arXiv:2002.08621},
year={2020}
}
Subspace Inference for Bayesian Deep Learning
Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson
ICML Workshop 2019: Uncertainty & Robustness in Deep Learning
Oral Presentation
@article{garipov16ttconv,
author={Garipov, Timur and Podoprikhin, Dmitry and Novikov, Alexander and Vetrov, Dmitry},
title={Ultimate tensorization: compressing convolutional and {FC} layers alike},
journal={arXiv preprint arXiv:1611.03214},
year={2016}
}
Contact-Aware Lyapunov Controller Design via Alternating Optimization
Richard Li and Timur Garipov
Improving Stability in Deep Reinforcement Learning with Weight Averaging
Evgenii Nikishin, Pavel Izmailov, Ben Athiwaratkun, Dmitrii Podoprikhin, Timur Garipov, Pavel Shvechikov, Dmitry Vetrov, and Andrew Gordon Wilson
Bayesian incremental learning for deep neural networks
Max Kochurov, Timur Garipov, Dmitry Podoprikhin, Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov
@article{kochurov2018bayesian,
title={Bayesian incremental learning for deep neural networks},
author={Kochurov, Max and Garipov, Timur and Podoprikhin, Dmitry and Molchanov, Dmitry and Ashukha, Arsenii and Vetrov, Dmitry},
journal={arXiv preprint arXiv:1802.07329},
year={2018}
}
