56 citations · 97 across the 12 of their papers we have counts for
13 papers
A Primal-Dual Approach to Solving Variational Inequalities with General Constraints
Tatjana Chavdarova, Tong Yang, Matteo Pagliardini +1
Yang et al. (2023) recently showed how to use first-order gradient methods to solve general variational inequalities (VIs) under a limiting assumption that analytic solutions of sp…
Continuous-time Analysis for Variational Inequalities: An Overview and Desiderata
Tatjana Chavdarova, Ya-Ping Hsieh, Michael I. Jordan
Algorithms that solve zero-sum games, multi-objective agent objectives, or, more generally, variational inequality (VI) problems are notoriously unstable on general problems. Owing…
Solving Constrained Variational Inequalities via a First-order Interior Point-based Method
Tong Yang, Michael I. Jordan, Tatjana Chavdarova
We develop an interior-point approach to solve constrained variational inequality (cVI) problems. Inspired by the efficacy of the alternating direction method of multipliers (ADMM)…
Improving Generalization via Uncertainty Driven Perturbations
Matteo Pagliardini, Gilberto Manunza, Martin Jaggi +2
Recently Shah et al., 2020 pointed out the pitfalls of the simplicity bias - the tendency of gradient-based algorithms to learn simple models - which include the model's high sensi…
The Peril of Popular Deep Learning Uncertainty Estimation Methods
Yehao Liu, Matteo Pagliardini, Tatjana Chavdarova +1
Uncertainty estimation (UE) techniques -- such as the Gaussian process (GP), Bayesian neural networks (BNN), Monte Carlo dropout (MCDropout) -- aim to improve the interpretability…
Last-Iterate Convergence of Saddle-Point Optimizers via High-Resolution Differential Equations
Tatjana Chavdarova, Michael I. Jordan, Manolis Zampetakis
Several widely-used first-order saddle-point optimization methods yield an identical continuous-time ordinary differential equation (ODE) that is identical to that of the Gradient…
Semantic Perturbations with Normalizing Flows for Improved Generalization
Oguz Kaan Yuksel, Sebastian U. Stich, Martin Jaggi +1
Data augmentation is a widely adopted technique for avoiding overfitting when training deep neural networks. However, this approach requires domain-specific knowledge and is often…
Convening during COVID-19: Lessons learnt from organizing virtual workshops in 2020
Mandana Samiei, Caroline Weis, Larissa Schiavo +2
This report is an account of the authors' experiences as organizers of WiML's "Un-Workshop" event at ICML 2020. Un-workshops focus on participant-driven structured discussions on a…
Taming GANs with Lookahead-Minmax
Tatjana Chavdarova, Matteo Pagliardini, Sebastian U. Stich +2
Generative Adversarial Networks are notoriously challenging to train. The underlying minmax optimization is highly susceptible to the variance of the stochastic gradient and the ro…
Reducing Noise in GAN Training with Variance Reduced Extragradient
Tatjana Chavdarova, Gauthier Gidel, François Fleuret +1
We study the effect of the stochastic gradient noise on the training of generative adversarial networks (GANs) and show that it can prevent the convergence of standard game optimiz…
SGAN: An Alternative Training of Generative Adversarial Networks
Tatjana Chavdarova, François Fleuret
The Generative Adversarial Networks (GANs) have demonstrated impressive performance for data synthesis, and are now used in a wide range of computer vision tasks. In spite of this…
The WILDTRACK Multi-Camera Person Dataset
Tatjana Chavdarova, Pierre Baqué, Stéphane Bouquet +6
People detection methods are highly sensitive to the perpetual occlusions among the targets. As multi-camera set-ups become more frequently encountered, joint exploitation of the a…
Deep Multi-camera People Detection
Tatjana Chavdarova, François Fleuret
This paper addresses the problem of multi-view people occupancy map estimation. Existing solutions for this problem either operate per-view, or rely on a background subtraction pre…