436 citations · 1.4k across the 41 of their papers we have counts for
17 papers · 1 filter
DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning
Jonathan Lebensold, Maziar Sanjabi, Pietro Astolfi +4
Text-to-image diffusion models have been shown to suffer from sample-level memorization, possibly reproducing near-perfect replica of images that they are trained on, which may be…
Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning
John Nguyen, Jianyu Wang, Kshitiz Malik +2
An oft-cited challenge of federated learning is the presence of heterogeneity. \emph{Data heterogeneity} refers to the fact that data from different clients may follow very differe…
Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano
Chuan Guo, Alexandre Sablayrolles, Maziar Sanjabi
Differential privacy (DP) is by far the most widely accepted framework for mitigating privacy risks in machine learning. However, exactly how small the privacy parameter needs…
Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning
John Nguyen, Jianyu Wang, Kshitiz Malik +2
An oft-cited challenge of federated learning is the presence of heterogeneity. \emph{Data heterogeneity} refers to the fact that data from different clients may follow very differe…
FedShuffle: Recipes for Better Use of Local Work in Federated Learning
Samuel Horváth, Maziar Sanjabi, Lin Xiao +2
The practice of applying several local updates before aggregation across clients has been empirically shown to be a successful approach to overcoming the communication bottleneck i…
Federated Learning with Partial Model Personalization
Krishna Pillutla, Kshitiz Malik, Abdelrahman Mohamed +3
We consider two federated learning algorithms for training partially personalized models, where the shared and personal parameters are updated either simultaneously or alternately…
Measuring Self-Supervised Representation Quality for Downstream Classification using Discriminative Features
Neha Kalibhat, Kanika Narang, Hamed Firooz +2
Self-supervised learning (SSL) has shown impressive results in downstream classification tasks. However, there is limited work in understanding their failure modes and interpreting…
BARACK: Partially Supervised Group Robustness With Guarantees
Nimit S. Sohoni, Maziar Sanjabi, Nicolas Ballas +4
While neural networks have shown remarkable success on classification tasks in terms of average-case performance, they often fail to perform well on certain groups of the data. Suc…
On Tilted Losses in Machine Learning: Theory and Applications
Tian Li, Ahmad Beirami, Maziar Sanjabi +1
Exponential tilting is a technique commonly used in fields such as statistics, probability, information theory, and optimization to create parametric distribution shifts. Despite i…
Tilted Empirical Risk Minimization
Tian Li, Ahmad Beirami, Maziar Sanjabi +1
Empirical risk minimization (ERM) is typically designed to perform well on the average loss, which can result in estimators that are sensitive to outliers, generalize poorly, or tr…
FedDANE: A Federated Newton-Type Method
Tian Li, Anit Kumar Sahu, Manzil Zaheer +3
Federated learning aims to jointly learn statistical models over massively distributed remote devices. In this work, we propose FedDANE, an optimization method that we adapt from D…
When Does Non-Orthogonal Tensor Decomposition Have No Spurious Local Minima?
Maziar Sanjabi, Sina Baharlouei, Meisam Razaviyayn +1
We study the optimization problem for decomposing dimensional fourth-order Tensors with non-orthogonal components. We derive \textit{deterministic} conditions under which s…
Fair Resource Allocation in Federated Learning
Tian Li, Maziar Sanjabi, Ahmad Beirami +1
Federated learning involves training statistical models in massive, heterogeneous networks. Naively minimizing an aggregate loss function in such a network may disproportionately a…
Training generative networks using random discriminators
Babak Barazandeh, Meisam Razaviyayn, Maziar Sanjabi
In recent years, Generative Adversarial Networks (GANs) have drawn a lot of attentions for learning the underlying distribution of data in various applications. Despite their wide…
Federated Optimization in Heterogeneous Networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer +3
Federated Learning is a distributed learning paradigm with two key challenges that differentiate it from traditional distributed optimization: (1) significant variability in terms…
On the Convergence and Robustness of Training GANs with Regularized Optimal Transport
Maziar Sanjabi, Jimmy Ba, Meisam Razaviyayn +1
Generative Adversarial Networks (GANs) are one of the most practical methods for learning data distributions. A popular GAN formulation is based on the use of Wasserstein distance…
Federated Multi-Task Learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi +1
Federated learning poses new statistical and systems challenges in training machine learning models over distributed networks of devices. In this work, we show that multi-task lear…