Search Results for author: Daogao Liu

Found 9 papers, 0 papers with code

Private Convex Optimization in General Norms

no code implementations18 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 $\normx{\cdot}$.

When Does Differentially Private Learning Not Suffer in High Dimensions?

no code implementations1 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.

Private Convex Optimization via Exponential Mechanism

no code implementations1 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.

Better Private Algorithms for Correlation Clustering

no code implementations22 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.

Private Non-smooth ERM and SCO in Subquadratic Steps

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.

Tight lower bounds for Differentially Private ERM

no code implementations29 Sep 2021 Daogao Liu, Zhou Lu

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

The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence

no code implementations28 Jun 2021 Daogao Liu, Zhou Lu

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

Lower Bounds for Differentially Private ERM: Unconstrained and Non-Euclidean

no code implementations28 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.

Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps

no code implementations29 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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