336 citations · 1.1k across the 77 of their papers we have counts for
11 papers · 1 filter
Adversarial Tradeoffs in Robust State Estimation
Thomas T. C. K. Zhang, Bruce D. Lee, Hamed Hassani +1
Adversarially robust training has been shown to reduce the susceptibility of learned models to targeted input data perturbations. However, it has also been observed that such adver…
Minimax Optimization: The Case of Convex-Submodular
Arman Adibi, Aryan Mokhtari, Hamed Hassani
Minimax optimization has been central in addressing various applications in machine learning, game theory, and control theory. Prior literature has thus far mainly focused on study…
Adversarial Robustness with Semi-Infinite Constrained Learning
Alexander Robey, Luiz F. O. Chamon, George J. Pappas +2
Despite strong performance in numerous applications, the fragility of deep learning to input perturbations has raised serious questions about its use in safety-critical domains. Wh…
Out-of-Distribution Robustness in Deep Learning Compression
Eric Lei, Hamed Hassani, Shirin Saeedi Bidokhti
In recent years, deep neural network (DNN) compression systems have proved to be highly effective for designing source codes for many natural sources. However, like many other mach…
Exploiting Heterogeneity in Robust Federated Best-Arm Identification
Aritra Mitra, Hamed Hassani, George Pappas
We study a federated variant of the best-arm identification problem in stochastic multi-armed bandits: a set of clients, each of whom can sample only a subset of the arms, collabor…
AutoEKF: Scalable System Identification for COVID-19 Forecasting from Large-Scale GPS Data
Francisco Barreras, Mikhail Hayhoe, Hamed Hassani +1
We present an Extended Kalman Filter framework for system identification and control of a stochastic high-dimensional epidemic model. The scale and severity of the COVID-19 emergen…
Robust Classification Under Attack for the Gaussian Mixture Model
Payam Delgosha, Hamed Hassani, Ramtin Pedarsani
It is well-known that machine learning models are vulnerable to small but cleverly-designed adversarial perturbations that can cause misclassification. While there has been major p…
Federated Functional Gradient Boosting
Zebang Shen, Hamed Hassani, Satyen Kale +1
In this paper, we initiate a study of functional minimization in Federated Learning. First, in the semi-heterogeneous setting, when the marginal distributions of the feature vector…
Model-Based Domain Generalization
Alexander Robey, George J. Pappas, Hamed Hassani
Despite remarkable success in a variety of applications, it is well-known that deep learning can fail catastrophically when presented with out-of-distribution data. Toward addressi…
Exploiting Shared Representations for Personalized Federated Learning
Liam Collins, Hamed Hassani, Aryan Mokhtari +1
Deep neural networks have shown the ability to extract universal feature representations from data such as images and text that have been useful for a variety of learning tasks. Ho…
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients
Aritra Mitra, Rayana Jaafar, George J. Pappas +1
We consider a standard federated learning (FL) architecture where a group of clients periodically coordinate with a central server to train a statistical model. We develop a genera…