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no code implementations • 7 Nov 2023 • Hilal Asi, Daogao Liu

We study differentially private stochastic convex optimization (DP-SCO) under user-level privacy, where each user may hold multiple data items.

no code implementations • 25 Oct 2023 • Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, Luke Zettlemoyer

Min-K% Prob can be applied without any knowledge about the pretraining corpus or any additional training, departing from previous detection methods that require training a reference model on data that is similar to the pretraining data.

no code implementations • 25 May 2023 • Yangsibo Huang, Haotian Jiang, Daogao Liu, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni

In this paper, we study the setting in which data owners train machine learning models collaboratively under a privacy notion called joint differential privacy [Kearns et al., 2018].

no code implementations • 21 Feb 2023 • Yangsibo Huang, Daogao Liu, Zexuan Zhong, Weijia Shi, Yin Tat Lee

Fine-tuning a language model on a new domain is standard practice for domain adaptation.

no code implementations • 13 Feb 2023 • Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian

The development of efficient sampling algorithms catering to non-Euclidean geometries has been a challenging endeavor, as discretization techniques which succeed in the Euclidean setting do not readily carry over to more general settings.

no code implementations • 1 Jan 2023 • Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian

We give a parallel algorithm obtaining optimization error $\epsilon_{\text{opt}}$ with $d^{1/3}\epsilon_{\text{opt}}^{-2/3}$ gradient oracle query depth and $d^{1/3}\epsilon_{\text{opt}}^{-2/3} + \epsilon_{\text{opt}}^{-2}$ gradient queries in total, assuming access to a bounded-variance stochastic gradient estimator.

1 code implementation • 21 Oct 2022 • Ziqi Wang, Yuexin Wu, Frederick Liu, Daogao Liu, Le Hou, Hongkun Yu, Jing Li, Heng Ji

However, these data augmentation methods either potentially cause shifts in decision boundaries (representation interpolation), are not expressive enough (token replacement), or introduce too much computational overhead (augmentation with models).

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 $\|\cdot\|$.

1 code implementation • 1 Jul 2022 • Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta

Large pretrained models can be privately fine-tuned to achieve performance approaching that of non-private models.

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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