Search Results for author:
Found 19 papers, 1 papers with code
Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages
We study the problem of private vector mean estimation in the shuffle model of privacy where $n$ users each have a unit vector $v^{(i)} \in\mathbb{R}^d$.
DP-Dueling: Learning from Preference Feedback without Compromising User Privacy
We consider the well-studied dueling bandit problem, where a learner aims to identify near-optimal actions using pairwise comparisons, under the constraint of differential privacy.
User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates
We study differentially private stochastic convex optimization (DP-SCO) under user-level privacy, where each user may hold multiple data items.
Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime
We also develop an adaptive algorithm for the small-loss setting with regret $O(L^\star\log d + \varepsilon^{-1} \log^{1. 5}{d})$ where $L^\star$ is the total loss of the best expert.
From Robustness to Privacy and Back
Thus, we conclude that for any low-dimensional task, the optimal error rate for $\varepsilon$-differentially private estimators is essentially the same as the optimal error rate for estimators that are robust to adversarially corrupting $1/\varepsilon$ training samples.
Private optimization in the interpolation regime: faster rates and hardness results
In non-private stochastic convex optimization, stochastic gradient methods converge much faster on interpolation problems -- problems where there exists a solution that simultaneously minimizes all of the sample losses -- than on non-interpolating ones; we show that generally similar improvements are impossible in the private setting.
Private Online Prediction from Experts: Separations and Faster Rates
Our lower bounds also show a separation between pure and approximate differential privacy for adaptive adversaries where the latter is necessary to achieve the non-private $O(\sqrt{T})$ regret.
How many labelers do you have? A closer look at gold-standard labels
The construction of most supervised learning datasets revolves around collecting multiple labels for each instance, then aggregating the labels to form a type of ``gold-standard.''.
Optimal Algorithms for Mean Estimation under Local Differential Privacy
We show that PrivUnit (Bhowmick et al. 2018) with optimized parameters achieves the optimal variance among a large family of locally private randomizers.
Adapting to Function Difficulty and Growth Conditions in Private Optimization
We develop algorithms for private stochastic convex optimization that adapt to the hardness of the specific function we wish to optimize.
Private Adaptive Gradient Methods for Convex Optimization
We study adaptive methods for differentially private convex optimization, proposing and analyzing differentially private variants of a Stochastic Gradient Descent (SGD) algorithm with adaptive stepsizes, as well as the AdaGrad algorithm.
Stochastic Bias-Reduced Gradient Methods
We develop a new primitive for stochastic optimization: a low-bias, low-cost estimator of the minimizer $x_\star$ of any Lipschitz strongly-convex function.
Private Stochastic Convex Optimization: Optimal Rates in $\ell_1$ Geometry
Stochastic convex optimization over an $\ell_1$-bounded domain is ubiquitous in machine learning applications such as LASSO but remains poorly understood when learning with differential privacy.
Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms
We study and provide instance-optimal algorithms in differential privacy by extending and approximating the inverse sensitivity mechanism.
Minibatch Stochastic Approximate Proximal Point Methods
In contrast to standard stochastic gradient methods, these methods may have linear speedup in the minibatch setting even for non-smooth functions.
Near Instance-Optimality in Differential Privacy
We develop two notions of instance optimality in differential privacy, inspired by classical statistical theory: one by defining a local minimax risk and the other by considering unbiased mechanisms and analogizing the Cramer-Rao bound, and we show that the local modulus of continuity of the estimand of interest completely determines these quantities.
Element Level Differential Privacy: The Right Granularity of Privacy
Differential Privacy (DP) provides strong guarantees on the risk of compromising a user's data in statistical learning applications, though these strong protections make learning challenging and may be too stringent for some use cases.
The importance of better models in stochastic optimization
Standard stochastic optimization methods are brittle, sensitive to stepsize choices and other algorithmic parameters, and they exhibit instability outside of well-behaved families of objectives.
Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity
We develop model-based methods for solving stochastic convex optimization problems, introducing the approximate-proximal point, or aProx, family, which includes stochastic subgradient, proximal point, and bundle methods.
