Tatjana Chavdarova
Assistant Professor
TU Wien

Hello, I’m Tatjana (/tatiana/). I'm an Assistant Professor at TU Wien in Vienna, Austria. My research focuses on learning dynamics in multi-agent and game-theoretic settings. I enjoy collaborating with students and colleagues on problems at the intersection of optimization, game theory, and machine learning, and I love uncovering simple insights in complex systems.
Prior to joining TU Wien, I was a Visiting Professor at Politecnico di Milano (PoliMi), DEIB, where I collaborated 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.

» Hiring interns, PhD and Postdocs --> application form.
» The Games in ML '25 course materials are available here.

Research Interests

• Learning in Games – How do AI models (e.g., neural networks) adapt and compete in strategic settings? Can we design better learning algorithms for stability and efficiency?
• Reinforcement Learning – learning dynamics in competitive multi-agent and actor-critic settings.
• Generative Modeling & Robustness – understanding and improving generalization and adversarial resilience in AI.
• Algorithmic Game Theory – modeling complex multi-agent systems such as from microeconomics, mechanism design, and developing learning algorithms.

» More broadly, I’m interested in how game-like interactions shape intelligence — not only in AI systems, but also in human cognition and society. (For fun, I recommend Kelly Clancy's talk on how games shape our world!)

News:

March, 2026: I’m teaching a MSc-level course on Games in Machine Learning at TU Wien, starting in March. Course materials will be updated version of this course.
Jan., 2026: I was elected as the WiML President! Excited to what comes next! ;)
Jan., 2026: I was awarded a VRG grant from the Vienna Science and Technology Fund (WWTF), and I will be joining TU Wien as an Assistant Professor. If you’re interested in joining my group (and living in Vienna!), you can apply here.
second-half, 2025: Dynamical systems tools for ML are in vogue! We're hosting a NeurIPS workshop on Dynamics at the Frontiers of Optimization, Sampling, and Games (DynaFront). I'm also excited for the special 20th anniversary edition of the WiML workshop -- stay tuned for announcements. See you in San Diego!
July, 2025: I'll be at ICML! We’ll be presenting 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. Looking forward to seeing you in Vancouver!
May, 2025: I’m teaching a PhD-level course on Games in Machine Learning at PoliMi, starting in late May. Course materials are available here!

🏅 Thanks to the Vienna Science and Technology Fund (WWTF) for supporting my research, through the generous Vienna Research Group grant (in 2026) of € 1.8 M.
🏅 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.

Selected Research

• Frequency-Based Hyperparameter Selection in Games
  Aniket Sanyal, Baraah A. M. Sidahmed, Rebekka Burkholz, Tatjana Chavdarova
  AISTATS, 2026 • BibTex @inproceedings{sanyal2026freq, title = {Frequency-Based Hyperparameter Selection in Games}, author = {Aniket Sanyal and Baraah A. M. Sidahmed and Rebekka Burkholz and Tatjana Chavdarova}, booktitle = {AISTATS}, year = {2026}, }
• (preprint) The Invisible Handshake: Tacit Collusion between Adaptive Market Agents
  Luigi Foscari, Emanuele Guidotti, Nicolò Cesa-Bianchi, Tatjana Chavdarova, Alfio Ferrara
  ArXiv, 2025 • Paper • BibTex @article{foscari2025invisiblehandshake, title = {The Invisible Handshake: Tacit Collusion between Adaptive Market Agents}, author = {Luigi Foscari and Emanuele Guidotti and Nicolò Cesa-Bianchi and Tatjana Chavdarova and Alfio Ferrara}, journal = {ArXiv:2510.15995}, year = {2025}, }
• (workshop) Understanding Lookahead Dynamics Through Laplace Transform
  Aniket Sanyal and Tatjana Chavdarova
  HiLD-2025 Workshop at ICML, 2025 • ArXiv • BibTex @article{sanyal2025, title = {Understanding Lookahead Dynamics Through Laplace Transform}, author = {Aniket Sanyal and Tatjana Chavdarova}, journal = {ArXiv:2506.13712}, year = {2025}, }
• Decoupled SGDA for Games with Intermittent Strategy Communication
  Ali Zindari, Parham Yazdkhasti, Anton Rodomanov, Tatjana Chavdarova, Sebastian U. Stich
  ICML, 2025 • Paper • BibTex @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}, }
• (preprint) Learning Variational Inequalities from Data: Fast Generalization Rates under Strong Monotonicity
  Eric Zhao, Tatjana Chavdarova, Michael I. Jordan
  ArXiv, 2024 • Paper • BibTex @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}, }
• (workshop) Variational Inequality Methods for Multi-Agent Reinforcement Learning:
  Performance and Stability Gains
  Baraah A. M. Sidahmed, Tatjana Chavdarova
  OWA-2024 Workshop at NeurIPS, 2024 • Paper • BibTex @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}, }
• (workshop) On the Hypomonotone Class of Variational Inequalities
  Khaled Alomars, Tatjana Chavdarova
  OPT-2024 Workshop at NeurIPS, 2024 • Paper • BibTex @article{alomar2024hypomonotone, title = {On the Hypomonotone Class of Variational Inequalities}, author = {Khaled Alomar and Tatjana Chavdarova}, journal = {ArXiv:2410.09182}, year = {2024}, }
• (workshop) Decoupled Stochastic Gradient Descent for N-Player Games
  Ali Zindari, Parham Yazdkhasti, Tatjana Chavdarova, Sebastian U. Stich
  ARLET Workshop at ICML, 2024 • Paper • BibTex @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}, }
• A Primal-Dual Approach to Solving Variational Inequalities with General Constraints
  Tatjana Chavdarova*, Tong Yang*, Matteo Pagliardini, Michael I. Jordan
  ICLR, 2024 • Paper • Poster • Code • BibTex @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}, }
• Last-Iterate Convergence of Saddle Point Optimizers via High-Resolution Differential Equations
  Tatjana Chavdarova*, Michael I. Jordan*, Manolis Zampetakis*
  MTA journal, 2023 & early version at OPT NeurIPS workshop, 2021 • Paper • ArXiv • Poster • BibTex @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}, }
• Solving Constrained Variational Inequalities via a First-order Interior Point-based Method—Notable Paper (top 25%)
  Tong Yang*, Michael I. Jordan*, Tatjana Chavdarova*
  ICLR, 2023 • Paper • Slides • Poster • Code • BibTex @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}, }
• (workshop) Continuous-time Analysis for Variational Inequalities: An Overview and Desiderata
  Tatjana Chavdarova*, Ya-Ping Hsieh*, Michael I. Jordan*
  Continuous time methods for ML, ICML workshop, 2022 • ArXiv • Poster • BibTex @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}, }
• (workshop) The Peril of Popular Deep Learning Uncertainty Estimation Methods
  Yehao Liu*, Matteo Pagliardini*, Tatjana Chavdarova, Sebastian U. Stich
  BDL NeurIPS workshop, 2021 • Paper • Code • Poster • BibTex @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}, }
• (workshop) Improved Adversarial Robustness via Uncertainty Targeted Attacks
  Gilberto Manunza*, Matteo Pagliardini*, Martin Jaggi, Tatjana Chavdarova
  UDL ICML workshop, 2021 • Paper • Poster • BibTex @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} }
• Semantic Perturbations with Normalizing Flows for Improved Generalization
  Oguz K Yüksel, Sebastian U. Stich, Martin Jaggi, Tatjana Chavdarova
  ICCV 2021 • Paper • Code • Video • BibTex @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} }
• Taming GANs with Lookahead-Minmax
  Tatjana Chavdarova*, Matteo Pagliardini*, Sebastian U. Stich, François Fleuret, Martin Jaggi
  ICLR 2021 •Paper •Code •Video •Poster •BibTex @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} }
• (PhD thesis) Deep Generative Models and Applications
  Tatjana Chavdarova, Jury: F. Fleuret, P. Frossard, L. Denoyer, S. Lacoste-Julien, M. Jaggi.
  EPFL, July 2020 •Thesis •BibTex @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}, }
• Reducing Noise in GAN Training with Variance Reduced Extragradient
  Tatjana Chavdarova*, Gauthier Gidel*, François Fleuret, Simon Lacoste-Julien
  NeurIPS 2019 •Paper •Code •Slides •Poster •Video •BibTex @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.}, }
  ‡Selected as oral at MBRL workshop @ICML, and at Montreal AI Symposium '19.
• SGAN: An Alternative Training of Generative Adversarial Networks
  Tatjana Chavdarova, François Fleuret
  CVPR 2018 •Paper •Slides •Poster •BibTex @inproceedings{chavdarova-fleuret-2018, author = {Chavdarova, T. and Fleuret, F.}, title = {{SGAN}: An Alternative Training of Generative Adversarial Networks}, booktitle = {CVPR}, year = {2018}, }
• WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection
  T. Chavdarova, P. Baqué, S. Bouquet, A. Maksai, T. Bagautdinov, L. Lettry, P. Fua, L. Van Gool, François Fleuret
  CVPR 2018 •Paper •Dataset •Code •Poster •BibTex @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.

Selected Talks

• Learning Dynamics in Multiplayer Games
  UT Austin, USA, November 2025. • slides • Abstract Intelligence often emerges through interaction and competition. Likewise, advanced AI algorithms often rely on competing learning objectives. Whether through data sampling, environmental interaction, or self-play, agents iteratively adapt their strategies in pursuit of an equilibrium—where the competing objectives are balanced. This talk explores the learning dynamics in multiplayer games, where multiple agents learn and adapt simultaneously. We will examine how these dynamics differ from single-agent optimization, addressing key challenges such as rotational learning dynamics, stochastic noise, and strategic constraints. Drawing on examples from machine learning— including robust optimization, generative adversarial networks, and multi-agent reinforcement learning—the talk will highlight the implications of these dynamics for understanding and designing modern learning systems.
  
• Learning Dynamics in Multiplayer Games
  CS Department, Bocconi University, Milano, Italy, November 2024. • slides
  DEIB, Polimi, Milano, Italy, October 2024. • slides
  
• Last-Iterate Convergence of Saddle-Point Optimizers via High-Resolution Differential Equations
  ISMP, Montreal, Canada, July 2024. • slides
  
• A First Order Primal-Dual Method for Solving Constrained Variational Inequalities
  20th EUROpt Workshop, Budapest, Hungary, August 2023. • slides
  
• Learning Dynamics in Multi-Player Games
  Robotics and Perception Group of Prof. Davide Scaramuzza, University of Zurich, Switzerland; July 2023.
  • slides
  
• A First Order Primal-Dual Method for Solving Constrained Variational Inequalities
  SIAM OP23, Seattle, WA; May 31, 2023. • abstract • slides
  ‡ My coauthor Manolis Zampetakis presented our work on HRDEs, June 1, 2023. • slides
• Beyond Standard Minimization: Solving for Equilibria in Constrained Variational Inequalities
  Symposium on Frontiers of Machine Learning and Artificial Intelligence, Viterbi School of Engineering, University of Southern California, Nov. 2022 • slides
• Optimization of differentiable games
  NSA Lab, Johns Hopkins University, April 2022 • slides
• Continious-time Tools for Min-max Optimization
  Simons Institute: Learning & Games Reading Group , March 2022 • slides
• Generative Adversarial Networks
  Mediterranean Machine Learning (M2L) school, Jan. 2021 • video • slides • colab
• An Introduction to JAX
  Feb. 2020 • slides • colab
• Reducing Noise in GAN Training with Variance Reduced Extragradient
  Selected as oral presentation at Montreal AI Symposium and at ICML workshop on Generative Modeling and Model-Based Reasoning for Robotics and AI, 2019 • slides
• Generative Adversarial Networks: Algorithm, Challenges & Research Topics
  Paris GANs & co meetup, Oct. 2018 • slides
• SGAN: An Alternative Training of Generative Adversarial Networks & Research Topics
  Idiap/HES-SO AI workshop, April 2018 • slides

Teaching

Student Supervision

Supervision & Teaching Assistance (up to 2023)

TAing

Blog Posts

High-Resolution ODEs

From discrete optimization methods to continuous time dynamics, using varying modeling precision.

Activities

Workshop Organization

• NeurIPS'25 workshop on Dynamics at the Frontiers of Optimization, Sampling, and Games (DynaFront). To be held at the Conference on Neural Information Processing Systems (NeurIPS). Dec, 2025.
• Advisory committee and WiML liaison for: WiML Un-Workshop @ ICML '21 and WiML Un-Workshop @ ICML '22. Held at the International Conference on Machine Learning (ICML). July, 2021 and July, 2022.
• Part of the Advisory Committee. ICCV Workshop on Multi-camera Multiple People Tracking, Oct., 2021.
• Co-organized WiML Virtual Un-Workshop as a Program Chair. Held at the International Conference on Machine Learning (ICML). July, 2020.

Socials

• Co-organized the Building Inclusive Communities social, jointly with Eirene Seiradaki, Ana Quintero and Mandana Samiei. Held at ICML, Vancouver, Canada. July, 2025.
• Co-organized the Women in ML Theory social, jointly with Claire Vernade. Held at the 36th International Conference on Algorithmic Learning Theory (ALT), Milano, Italy. Februray, 2025.
• Co-organized the WiML-CWS Social @ AISTATS 2022, on behalf of WiML. Held at the AISTATS. March, 2022.
• Co-organized the WiML-CWS Event: Community-Driven Mentoring Event and Panel, on behalf of WiML. Held at the AISTATS. April, 2021.

Guest Editor

• Minimax Theory and its Applications. Special issue on min-max and Variational Inequality problems.
  Jointly with Gauthier Gidel, Manolis Zampetakis, Michael Jordan. Since 2024.

Reviewing

• Conferences: • ICLR • ICML • AISTATS • NeurIPS
• Journals: • JMLR • TMLR • SIAM Journal on Mathematics of Data Science • Springer Nature Machine Learning
• Workshops: • Optimization for Machine Learning (at NeurIPS)

Reading Group

• Organizer of the Smooth Games Reading Group at EPFL. Oct. 2020 - March 2021.

Misc

• President of Women in Machine Learning. Since January 2026.
• Vice President of Women in Machine Learning. April 2023 - January 2026.
• Board of Directors for Women in Machine Learning. Since April 2021.

Panels

• At the WiML workshop, at NeurIPS 2024. With Angelica Lim, Thea Klaeboe Aarrestad, Isabelle Guyon, moderated by Celestine Mendler-Dünner. Dec. 2024.
• At the Continuous time methods for ML workshop, at ICML 2022. With Ricky Chen, Priya Donti, Adil Salim, moderated by Michael Arbel. July 2022.

Contact

Email: tatjana.chavdarova[at]tuwien[dot]ac[dot]at

Office FB 02 05, second floor,
Dekanat der Fakultät für Informatik, TU Wien
Karlsplatz 13, 1040 Wien