no code implementations • 13 Dec 2023 • Ilana Sebag, Muni Sreenivas Pydi, Jean-Yves Franceschi, Alain Rakotomamonjy, Mike Gartrell, Jamal Atif, Alexandre Allauzen
Safeguarding privacy in sensitive training data is paramount, particularly in the context of generative modeling.
no code implementations • 1 Nov 2023 • Alexandre Verine, Muni Sreenivas Pydi, Benjamin Negrevergne, Yann Chevaleyre
Rejection sampling methods have recently been proposed to improve the performance of discriminator-based generative models.
Ranked #14 on Image Generation on CelebA 64x64
no code implementations • 1 Feb 2023 • Alexandre Verine, Benjamin Negrevergne, Muni Sreenivas Pydi, Yann Chevaleyre
Generative models can have distinct mode of failures like mode dropping and low quality samples, which cannot be captured by a single scalar metric.
no code implementations • 30 Jan 2023 • Eirini Ioannou, Muni Sreenivas Pydi, Po-Ling Loh
A new variant of Newton's method for empirical risk minimization is studied, where at each iteration of the optimization algorithm, the gradient and Hessian of the objective function are replaced by robust estimators taken from existing literature on robust mean estimation for multivariate data.
no code implementations • NeurIPS 2021 • Muni Sreenivas Pydi, Varun Jog
Adversarial risk quantifies the performance of classifiers on adversarially perturbed data.
no code implementations • ICML 2020 • Muni Sreenivas Pydi, Varun Jog
We show that the optimal adversarial risk for binary classification with 0-1 loss is determined by an optimal transport cost between the probability distributions of the two classes.
no code implementations • 10 Oct 2019 • Muni Sreenivas Pydi, Vishnu Suresh Lokhande
We consider an active learning setting where the algorithm has access to a large pool of unlabeled data and a small pool of labeled data.
no code implementations • 13 Feb 2018 • Muni Sreenivas Pydi, Varun Jog, Po-Ling Loh
We also provide simulations showing the relative convergence rates of our algorithms in comparison to an unbiased random walk, as a function of the smoothness of the graph function.
no code implementations • 12 Feb 2017 • Muni Sreenivas Pydi, Ambedkar Dukkipati
Spectral clustering is one of the most popular methods for community detection in graphs.