
Private Stochastic Convex Optimization: Optimal Rates in ℓ_1 Geometry
Stochastic convex optimization over an ℓ_1bounded domain is ubiquitous ...
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Lossless Compression of Efficient Private Local Randomizers
Locally Differentially Private (LDP) Reports are commonly used for colle...
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Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling
Recent work of Erlingsson, Feldman, Mironov, Raghunathan, Talwar, and Th...
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When is Memorization of Irrelevant Training Data Necessary for HighAccuracy Learning?
Modern machine learning models are complex and frequently encode surpris...
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On the Error Resistance of Hinge Loss Minimization
Commonly used classification algorithms in machine learning, such as sup...
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Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC
Various differentially private algorithms instantiate the exponential me...
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Stochastic Optimization with Laggard Data Pipelines
Stateoftheart optimization is steadily shifting towards massively par...
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Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses
Uniform stability is a notion of algorithmic stability that bounds the w...
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Private Stochastic Convex Optimization: Optimal Rates in Linear Time
We study differentially private (DP) algorithms for stochastic convex op...
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Exploring the MemorizationGeneralization Continuum in Deep Learning
Human learners appreciate that some facts demand memorization whereas ot...
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Computational Separations between Sampling and Optimization
Two commonly arising computational tasks in Bayesian learning are Optimi...
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Rényi Differential Privacy of the Sampled Gaussian Mechanism
The Sampled Gaussian Mechanism (SGM)a composition of subsampling and ...
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Private Stochastic Convex Optimization with Optimal Rates
We study differentially private (DP) algorithms for stochastic convex op...
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SemiCyclic Stochastic Gradient Descent
We consider convex SGD updates with a blockcyclic structure, i.e. where...
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Better Algorithms for Stochastic Bandits with Adversarial Corruptions
We study the stochastic multiarmed bandits problem in the presence of a...
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Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity
Sensitive statistics are often collected across sets of users, with repe...
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Private Selection from Private Candidates
Differentially Private algorithms often need to select the best amongst ...
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Privacy Amplification by Iteration
Many commonly used learning algorithms work by iteratively updating an i...
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Online Linear Quadratic Control
We study the problem of controlling linear timeinvariant systems with k...
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Adversarially Robust Generalization Requires More Data
Machine learning models are often susceptible to adversarial perturbatio...
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Online learning over a finite action set with limited switching
This paper studies the value of switching actions in the Prediction From...
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Scalable Private Learning with PATE
The rapid adoption of machine learning has increased concerns about the ...
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On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches
The recent, remarkable growth of machine learning has led to intense int...
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Semisupervised Knowledge Transfer for Deep Learning from Private Training Data
Some machine learning applications involve training data that is sensiti...
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Deep Learning with Differential Privacy
Machine learning techniques based on neural networks are achieving remar...
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TensorFlow: LargeScale Machine Learning on Heterogeneous Distributed Systems
TensorFlow is an interface for expressing machine learning algorithms, a...
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Private Empirical Risk Minimization Beyond the Worst Case: The Effect of the Constraint Set Geometry
Empirical Risk Minimization (ERM) is a standard technique in machine lea...
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Kunal Talwar
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