About me
I am a Postdoctoral Researcher at the Department of Electrical Engineering and Computer Science (EECS) at the University of California at Berkeley, working with Michael Jordan. I am very grateful to the Swiss National Science Foundation for supporting my research, through the Early.Postdoc.Mobility (in 2021) and the Postdoc.Mobility (in 2023) fellowships.
Prior to my current position, I was Postdoctoral Research Scientist at the Machine Learning and Optimization (MLO) lab at EPFL, working with Martin Jaggi. While at EPFL, I organized the Smooth Games reading group . In part of my time, I participated in the intelligent Global Health (iGH) sub-group of MLO led by Mary-Anne Hartley, by advising on the machine learning aspect of the ongoing projects. I obtained my Ph.D. from EPFL, and Idiap, supervised by François Fleuret. During my Ph.D. studies I did two internships at: (i) Mila where I was supervised by Yoshua Bengio and Simon Lacoste-Julien, as well as at (ii) DeepMind supervised by Irina Higgins.
I am on the job market ('22/'23). Please reach out if you think I will be a good fit for your department. Thanks!
Research interests
My main interests are at the intersection of game theory and machine learning. My research aims to better understand the training dynamics of multi-player games and develop improved methods for their optimization. In addition, my broader interests include unsupervised learning, generative modeling, generalization, and robustness.
Other
I am very passionate about cultivating a diverse and inclusive ML community, and I actively participate in various activities, most prominently as part of the board of directors of WiML. Outside work, I enjoy hiking, (acro)yoga, biking, and playing games. I love traveling to places with distinct nature (and taking photos :) ).
Selected Research Projects
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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 (for WiML@ICML'22) • Code • BibTex@inproceedings{yang2022acvi, 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}, } -
(preprint)
Revisiting the ACVI Method for Constrained Variational Inequalities
Tatjana Chavdarova*, Matteo Pagliardini*, Tong Yang*, Michael I. Jordan*
preprint, 2022 • ArXiv • Poster (CLIMB '22) • BibTex@article{chavdarova2022acvi, title = {Revisiting the ACVI Method for Constrained Variational Inequalities}, author = {Chavdarova, Tatjana and Pagliardini, Matteo and Yang, Tong and Jordan, Michael I.}, journal= {ArXiv:2210.15659}, year = {2022}, } -
(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)
Last-Iterate Convergence of Saddle Point Optimizers via High-Resolution Differential Equations
Tatjana Chavdarova*, Michael I. Jordan*, Manolis Zampetakis*
OPT NeurIPS workshop, 2021 • Paper • ArXiv • Poster • BibTex@article{chavdarova2021hrdes, title = {Last-Iterate Convergence of Saddle Point Optimizers via High-Resolution Differential Equations}, author = {Tatjana Chavdarova and Michael I. Jordan and Manolis Zampetakis}, booktitle= {NeurIPS Workshop on Optimization for Machine Learning}, year = {2021}, } -
(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.
Past Talks
- 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
Former Supervision & Teaching Assistance
- Gilberto Manunza (MSc thesis), On the Connection between Adversarial Training and Uncertainty Estimation seven months MSc project, 2021, EPFL.
- Apostolov Alexander (CS-498 Semester Project), On the Effect of Variance Reduced Gradient and Momentum for Optimizing Deep Neural Networks autumn semester, 2020, EPFL.
- Co-superivison with Sebastian U. Stich:
- Oğuz Kaan Yüksel, Normalizing Flows for Generalization and Robustness (CS-498 Semester Project) autumn semester, 2020, EPFL.
- Yehao Liu, On the Drawbacks of Popular Deep Learning Uncertainty Estimation Methods spring semester & summer internship, 2021, EPFL.
- Co-superivison with Mary-Anne Hartley:
- Deeksha M. Shama, Deep Learning Approaches for Covid-19 Diagnosis via Digital Lung Auscultation autumn semester, 2020.
- Pablo Cañas, On Uncertainty Estimation of Global COVID Policy Simulator autumn semester, 2020.
- Teaching Assistant, Deep Learning Course (EE-559) for MSc students, EPFL, 2018 & 2020.
- Teaching Assistant, An Introduction to Deep Learning for MSc students, African Master's in Machine Intelligence, Kigali, Rwanda, 2018.
Activities
Reviewing
- Conferences: • ICLR • ICML • AISTATS • NeurIPS
- Journals: • JMLR • TMLR • SIAM Journal on Mathematics of Data Science
- Workshops: • Optimization for Machine Learning (at NeurIPS)
Workshops
- 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 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.
Reading Group
- Organizer of the Smooth Games Reading Group at EPFL. Oct. 2020 - March 2021.
Misc
- (Incoming) Vice President of Women in Machine Learning. Starting in April 2023.
- Board of Directors for Women in Machine Learning. Since April 2021.
Panel
- Joined the panel at the Continuous time methods for ML workshop at ICML 2022. With Ricky Chen, Priya Donti, Adil Salim, and Michael Arbel. July 2022.
Contact
If you'd like to discuss my work or broadly on doing research in CS, DEI activities, etc., please don't hesitate to reach out at: tatjana.chavdarova[at]berkeley[dot]edu