1 code implementation • 4 Mar 2024 • Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin A Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
Lin et al. (2024) recently introduced the Private Evolution (PE) algorithm to generate DP synthetic images with only API access to diffusion models.
no code implementations • 29 May 2023 • Haotian Jiang, Qianxiao Li
We present a theoretical analysis of the approximation properties of convolutional architectures when applied to the modeling of temporal sequences.
no code implementations • 29 May 2023 • Haotian Jiang, Qianxiao Li
The Transformer architecture is widely applied in sequence modeling applications, yet the theoretical understanding of its working principles remains limited.
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 • 27 Feb 2023 • Haotian Jiang, Qianxiao Li, Zhong Li, Shida Wang
We survey current developments in the approximation theory of sequence modelling in machine learning.
no code implementations • 7 Aug 2022 • Sally Dong, Haotian Jiang, Yin Tat Lee, Swati Padmanabhan, Guanghao Ye
In this work, we give an algorithm that minimizes the above convex formulation to $\epsilon$-accuracy in $\widetilde{O}(\sum_{i=1}^n d_i \log (1 /\epsilon))$ gradient computations, with no assumptions on the condition number.
no code implementations • 20 May 2022 • Venkat Varada, Mina Ghashami, Jitesh Mehta, Haotian Jiang, Kurtis Voris
Relevance generation and 2.
no code implementations • ICLR 2022 • Zhong Li, Haotian Jiang, Qianxiao Li
Our results provide the theoretical understanding of approximation properties of the recurrent encoder-decoder architecture, which characterises, in the considered setting, the types of temporal relationships that can be efficiently learned.
no code implementations • 20 Jul 2021 • Haotian Jiang, Zhong Li, Qianxiao Li
We study the approximation properties of convolutional architectures applied to time series modelling, which can be formulated mathematically as a functional approximation problem.
no code implementations • 8 Apr 2020 • Haotian Jiang, Yin Tat Lee, Zhao Song, Sam Chiu-wai Wong
We propose a new cutting plane algorithm that uses an optimal $O(n \log (\kappa))$ evaluations of the oracle and an additional $O(n^2)$ time per evaluation, where $\kappa = nR/\epsilon$.
no code implementations • 19 May 2017 • Haotian Jiang, Jian Li, Mingda Qiao
In the Best-$K$ identification problem (Best-$K$-Arm), we are given $N$ stochastic bandit arms with unknown reward distributions.