
High-Resolution ODEs
From discrete optimization methods to continuous time dynamics, using varying modeling precision.
Hello! I’m Tatjana (pronounced /tatiana/), currently a Visiting Professor in the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano (Polimi), where I research and teach in the areas of game theory, machine learning, variational inequalities, multi-agent systems, and optimization algorithms. I collaborate with Nicola Gatti and Nicolò Cesa-Bianchi. I hold a Ph.D. from EPFL, and Idiap, advised by François Fleuret. During my doctoral studies, I interned at (i) Mila with Yoshua Bengio and Simon Lacoste-Julien, and at (ii) DeepMind under Irina Jurenka (Higgins). After my Ph.D., I was a Postdoctoral Researcher at EPFL’s MLO Lab with Martin Jaggi, and later at UC Berkeley EECS with Michael Jordan.
» We’re excited to host the DynaFront Workshop at NeurIPS 2025! Visit the workshop website for details. Paper submissions and reviewer self-nominations are now open!
» The Games in ML '25 course materials are available here.
I work at the intersection of game theory and machine learning, developing AI systems that learn, strategize, and interact in multi-agent environments. My research spans:
» Outside of research, I’m passionate about the broader implications of games—not just for AI, but for developing human inteligence and society. (For fun: I recommend this talk by Kelly Clancy on how games shape our world!)
second-half, 2025: Dynamical systems tools for ML are in vogue! We're hosting a NeurIPS workshop titled Dynamics at the Frontiers of Optimization, Sampling, and Games (DynaFront). Submission is now open! July, 2025: I'll be at ICML! Come check out our work on game-theoretic federated learning, where agents operate without full knowledge of others' strategies. We're also organizing the WiML workshop/symposium on Wednesday and co-hosting a social event on Thursday—plus more! Looking forward to seeing you in Vancouver! May, 2025: I am giving "Games in Machine Learning" PhD-level course at PoliMi, starting from late May. Course materials are available here! Sept., 2024: Very excited to be working with Nicola Gatti, Alberto Marchesi, Matteo Castiglioni, and the remaining game theory and reinforcement learning team at PoliMi and Milano! July, 2024: I will be at the ISMP conference and talk about HRDEs for VIs (slides below). See you in Montrèal! PS - if you're attending ICML, don't miss out WiML's events! Jan, 2024: You have two- or multi-player problem with constraints? use our ACVI method -> accepted at ICLR 2024. See you in Vienna!
🏅 Thanks to the Swiss National Science Foundation for supporting my research, through the Early.Postdoc.Mobility (in 2021) and the Postdoc.Mobility (in 2023) fellowships.
@article{sanyal2025,
title = {Understanding Lookahead Dynamics Through Laplace Transform},
author = {Aniket Sanyal and Tatjana Chavdarova},
journal = {ArXiv:2506.13712},
year = {2025},
}
@inproceedings{zindari2025decoupled,
title = {Decoupled SGDA for Games with Intermittent Strategy Communication},
author = {Ali Zindari and Parham Yazdkhasti and Anton Rodomanov and Tatjana Chavdarova and Sebastian U Stich},
booktitle = {ICML},
year = {2025},
}
@article{zhao2024learningvariationalinequalities,
title = {Learning Variational Inequalities from Data: Fast Generalization Rates under Strong Monotonicity},
author = {Eric Zhao and Tatjana Chavdarova and Michael I. Jordan},
journal = {ArXiv:2410.20649},
year = {2024},
}
@article{sidahmed2024vimarl,
title = {Variational Inequality Methods for Multi-Agent Reinforcement Learning: Performance and Stability Gains},
author = {Baraah A. M. Sidahmed and Tatjana Chavdarova},
journal = {ArXiv:2410.07976},
year = {2024},
}
@article{alomar2024hypomonotone,
title = {On the Hypomonotone Class of Variational Inequalities},
author = {Khaled Alomar and Tatjana Chavdarova},
journal = {ArXiv:2410.09182},
year = {2024},
}
@article{zindari2024decoupled,
title = {Decoupled Stochastic Gradient Descent for N-Player Games},
author = {Ali Zindari and Parham Yazdkhasti and Tatjana Chavdarova and Sebastian U Stich},
journal = {ICML 2024 Workshop: Aligning Reinforcement Learning Experimentalists and Theorists},
year = {2024},
}
@inproceedings{chavdarova2024acvi,
title = {A Primal-dual Approach for Solving Variational Inequalities with General-form Constraints},
author = {Chavdarova, Tatjana and Yang, Tong and Pagliardini, Matteo and Jordan, Michael I.},
booktitle = {ICLR},
year = {2024},
}
@article{chavdarova2023hrdes,
title = {Last-Iterate Convergence of Saddle Point Optimizers via High-Resolution Differential Equations},
author = {Tatjana Chavdarova and Michael I. Jordan and Manolis Zampetakis},
journal = {Minimax Theory and its Applications},
year = {2023},
}
@inproceedings{yang2023acvi,
title = {Solving Constrained Variational Inequalities via a First-order Interior Point-based Method},
author = {Tong Yang and Michael I. Jordan and Tatjana Chavdarova},
booktitle = {ICLR},
url={https://openreview.net/forum?id=RQY2AXFMRiu},
year = {2023},
}
@article{chavdarova2022contVIs,
title = {Continuous-time Analysis for Variational Inequalities: An Overview and Desiderata},
author = {Tatjana Chavdarova and Ya-Ping Hsieh and Michael I. Jordan},
booktitle= {ICML Workshop on Continuous time methods for Machine Learning},
year = {2022},
}
@article{liu2021peril,
title = {The Peril of Popular Deep Learning Uncertainty Estimation Methods},
author = {Yehao Liu and Matteo Pagliardini and Tatjana Chavdarova and Sebastian U. Stich},
journal = {NeurIPS Workshop on Bayesian Deep Learning},
year = {2021},
}
@article{ManunzaPagliardini2021,
title = {Improved Adversarial Robustness via Uncertainty Targeted Attacks},
author = {Gilberto Manunza and Matteo Pagliardini and Martin Jaggi and Tatjana Chavdarova},
journal = {ICML Workshop on Uncertainty and Robustness in Deep Learning},
year = {2021},
url = {http://www.gatsby.ucl.ac.uk/~balaji/udl2021/accepted-papers/UDL2021-paper-096.pdf}
}
@InProceedings{Yuksel_2021_ICCV,
author = {Y\"uksel, Oguz Kaan and Stich, Sebastian U. and Jaggi, Martin and Chavdarova, Tatjana},
title = {Semantic Perturbations With Normalizing Flows for Improved Generalization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {6619-6629}
}
@inproceedings{chavdarova2021lagan,
author = {Tatjana Chavdarova and
Matteo Pagliardini and
Sebastian U. Stich and
Fran{\c{c}}ois Fleuret and
Martin Jaggi},
title = {Taming GANs with Lookahead-Minmax},
booktitle = {{ICLR}},
publisher = {OpenReview.net},
year = {2021}
}
@article{Chavdarova:278463,
title = {Deep Generative Models and Applications},
author = {Chavdarova, Tatjana},
institution = {IEL},
publisher = {EPFL},
address = {Lausanne},
pages = {169},
year = {2020},
url = {http://infoscience.epfl.ch/record/278463},
doi = {10.5075/epfl-thesis-10257},
}
@inproceedings{chavdarova2019,
Author = {Tatjana Chavdarova and Gauthier Gidel and François Fleuret and Simon Lacoste-Julien},
Title = {Reducing Noise in {GAN} Training with Variance Reduced Extragradient},
Booktitle = {{Advances in Neural Information Processing Systems (NeurIPS)}},
Year = {2019},
volume = {32},
publisher = {Curran Associates, Inc.},
}
@inproceedings{chavdarova-fleuret-2018,
author = {Chavdarova, T. and Fleuret, F.},
title = {{SGAN}: An Alternative Training of Generative Adversarial Networks},
booktitle = {CVPR},
year = {2018},
}
@inproceedings{chavdarova-et-al-2018,
author = {Chavdarova, T. and Baqué, P. and Bouquet, S. and Maksai, A. and Jose, C. and Bagautdinov, T. and Lettry, L. and Fua, P. and Van Gool, L. and Fleuret, F.},
title = {{WILDTRACK}: A Multi-camera {HD} Dataset for Dense Unscripted Pedestrian Detection},
booktitle = {CVPR},
year = {2018},
pages = {5030-5039},
}
* Equal contributions.
Email: tatjana.chavdarova[at]polimi[dot]it
Office 20, First floor, Building 21 Via Golgi 39, 20133, Milan Dipartimento di Elettronica, Informazione e Bioingegneria Politecnico di Milano