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Found 8 papers, 1 papers with code
Randomization matters How to defend against strong adversarial attacks
We demonstrate the non-existence of a Nash equilibrium in our game when the classifier and the adversary are both deterministic, hence giving a negative answer to the above question in the deterministic regime.
Boosting Robustness by Clipping Gradients in Distributed Learning
However, we show that it is possible to circumvent the lower bound, and improve the learning performance, when the workers' gradients at model initialization are assumed to be bounded.
Overcoming the Challenges of Batch Normalization in Federated Learning
Batch normalization has proven to be a very beneficial mechanism to accelerate the training and improve the accuracy of deep neural networks in centralized environments.
Tackling Byzantine Clients in Federated Learning
The natural approach to robustify FL against adversarial clients is to replace the simple averaging operation at the server in the standard $\mathsf{FedAvg}$ algorithm by a \emph{robust averaging rule}.
An $α$-No-Regret Algorithm For Graphical Bilinear Bandits
We propose the first regret-based approach to the Graphical Bilinear Bandits problem, where $n$ agents in a graph play a stochastic bilinear bandit game with each of their neighbors.
Refined bounds for randomized experimental design
Experimental design is an approach for selecting samples among a given set so as to obtain the best estimator for a given criterion.
Best Arm Identification in Graphical Bilinear Bandits
We study the best arm identification problem in which the learner wants to find the graph allocation maximizing the sum of the bilinear rewards.
Randomization matters. How to defend against strong adversarial attacks
We demonstrate the non-existence of a Nash equilibrium in our game when the classifier and the Adversary are both deterministic, hence giving a negative answer to the above question in the deterministic regime.
