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no code implementations • 18 Jul 2022 • Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian

We propose a new framework for differentially private optimization of convex functions which are Lipschitz in an arbitrary norm $\normx{\cdot}$.

no code implementations • 1 Jul 2022 • Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta

To precisely characterize this for private convex learning, we introduce a condition on the objective that we term restricted Lipschitz continuity and derive improved bounds for the excess empirical and population risks that are dimension-independent under additional conditions.

no code implementations • 1 Mar 2022 • Sivakanth Gopi, Yin Tat Lee, Daogao Liu

Furthermore, we show how to implement this mechanism using $\widetilde{O}(n \min(d, n))$ queries to $f_i(x)$ for the DP-SCO where $n$ is the number of samples/users and $d$ is the ambient dimension.

no code implementations • 22 Feb 2022 • Daogao Liu

In machine learning, correlation clustering is an important problem whose goal is to partition the individuals into groups that correlate with their pairwise similarities as much as possible.

no code implementations • NeurIPS 2021 • Janardhan Kulkarni, Yin Tat Lee, Daogao Liu

We study the differentially private Empirical Risk Minimization (ERM) and Stochastic Convex Optimization (SCO) problems for non-smooth convex functions.

no code implementations • 29 Sep 2021 • Daogao Liu, Zhou Lu

We consider the lower bounds of differentially private ERM for general convex functions.

no code implementations • 28 Jun 2021 • Daogao Liu, Zhou Lu

The best known lower bounds, however, are worse than the upper bounds by a factor of $\log T$.

no code implementations • 28 May 2021 • Daogao Liu, Zhou Lu

We consider the lower bounds of differentially private empirical risk minimization (DP-ERM) for convex functions in constrained/unconstrained cases with respect to the general $\ell_p$ norm beyond the $\ell_2$ norm considered by most of the previous works.

no code implementations • 29 Mar 2021 • Janardhan Kulkarni, Yin Tat Lee, Daogao Liu

More precisely, our differentially private algorithm requires $O(\frac{N^{3/2}}{d^{1/8}}+ \frac{N^2}{d})$ gradient queries for optimal excess empirical risk, which is achieved with the help of subsampling and smoothing the function via convolution.

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