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07/20/2023
From Adaptive Query Release to Machine Unlearning
We formalize the problem of machine unlearning as design of efficient un...
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07/20/2023
Private Federated Learning with Autotuned Compression
We propose new techniques for reducing communication in private federate...
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06/18/2022
Adversarial Robustness is at Odds with Lazy Training
Recent works show that random neural networks are vulnerable against adv...
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06/02/2022
Faster Rates of Convergence to Stationary Points in Differentially Private Optimization
We study the problem of approximating stationary points of Lipschitz and...
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05/06/2022
Differentially Private Generalized Linear Models Revisited
We study the problem of (ϵ,δ)-differentially private learning of linear ...
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02/25/2021
Machine Unlearning via Algorithmic Stability
We study the problem of machine unlearning and identify a notion of algo...
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07/15/2020
FetchSGD: Communication-Efficient Federated Learning with Sketching
Existing approaches to federated learning suffer from a communication bo...
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02/22/2020
Private Stochastic Convex Optimization: Efficient Algorithms for Non-smooth Objectives
In this paper, we revisit the problem of private stochastic convex optim...
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07/17/2019
Improved Algorithms for Time Decay Streams
In the time-decay model for data streams, elements of an underlying data...
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03/12/2019
Communication-efficient distributed SGD with Sketching
Large-scale distributed training of neural networks is often limited by ...
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08/02/2018
Streaming Kernel PCA with Õ(√(n)) Random Features
We study the statistical and computational aspects of kernel principal c...
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07/18/2018
