no code implementations • 23 Apr 2024 • Gavin Brown, Jonathan Hayase, Samuel Hopkins, Weihao Kong, Xiyang Liu, Sewoong Oh, Juan C. Perdomo, Adam Smith
We present a sample- and time-efficient differentially private algorithm for ordinary least squares, with error that depends linearly on the dimension and is independent of the condition number of $X^\top X$, where $X$ is the design matrix.
1 code implementation • NeurIPS 2023 • Ruiying Lu, Yujie Wu, Long Tian, Dongsheng Wang, Bo Chen, Xiyang Liu, Ruimin Hu
First, instead of learning the continuous representations, we preserve the typical normal patterns as discrete iconic prototypes, and confirm the importance of Vector Quantization in preventing the model from falling into the shortcut.
no code implementations • ICCV 2023 • Long Tian, Jingyi Feng, Wenchao Chen, Xiaoqiang Chai, Liming Wang, Xiyang Liu, Bo Chen
Transductive Few-Shot Learning (TFSL) has recently attracted increasing attention since it typically outperforms its inductive peer by leveraging statistics of query samples.
no code implementations • 30 Jan 2023 • Xiyang Liu, Prateek Jain, Weihao Kong, Sewoong Oh, Arun Sai Suggala
Under label-corruption, this is the first efficient linear regression algorithm to guarantee both $(\varepsilon,\delta)$-DP and robustness.
no code implementations • 16 Jan 2023 • Mohammad Vahid Jamali, Xiyang Liu, Ashok Vardhan Makkuva, Hessam Mahdavifar, Sewoong Oh, Pramod Viswanath
Next, we derive the soft-decision based version of our algorithm, called soft-subRPA, that not only improves upon the performance of subRPA but also enables a differentiable decoding algorithm.
no code implementations • 27 May 2022 • Xiyang Liu, Weihao Kong, Prateek Jain, Sewoong Oh
For sub-Gaussian data, we provide nearly optimal statistical error rates even for $n=\tilde O(d)$.
no code implementations • 12 Nov 2021 • Xiyang Liu, Weihao Kong, Sewoong Oh
The key insight is that if we design an exponential mechanism that accesses the data only via one-dimensional robust statistics, then the resulting local sensitivity can be dramatically reduced.
1 code implementation • 29 Aug 2021 • Ashok Vardhan Makkuva, Xiyang Liu, Mohammad Vahid Jamali, Hessam Mahdavifar, Sewoong Oh, Pramod Viswanath
In this paper, we construct KO codes, a computationaly efficient family of deep-learning driven (encoder, decoder) pairs that outperform the state-of-the-art reliability performance on the standardized AWGN channel.
1 code implementation • NeurIPS 2021 • Xiyang Liu, Weihao Kong, Sham Kakade, Sewoong Oh
In statistical learning and analysis from shared data, which is increasingly widely adopted in platforms such as federated learning and meta-learning, there are two major concerns: privacy and robustness.
no code implementations • 2 Feb 2021 • Mohammad Vahid Jamali, Xiyang Liu, Ashok Vardhan Makkuva, Hessam Mahdavifar, Sewoong Oh, Pramod Viswanath
To lower the complexity of our decoding algorithm, referred to as subRPA in this paper, we investigate different ways for pruning the projections.
Information Theory Information Theory
no code implementations • 11 Sep 2020 • Yixi Xu, Sumit Mukherjee, Xiyang Liu, Shruti Tople, Rahul Dodhia, Juan Lavista Ferres
In this work, we propose the first formal framework for membership privacy estimation in generative models.
1 code implementation • ECCV 2020 • Xiyang Liu, Jie Yang, Wenrui Ding
The crowd counting task aims at estimating the number of people located in an image or a frame from videos.
1 code implementation • NeurIPS 2019 • Xiyang Liu, Sewoong Oh
We pose it as a property estimation problem, and study the fundamental trade-offs involved in the accuracy in estimated privacy guarantees and the number of samples required.
2 code implementations • 24 May 2019 • Xiyang Liu, Sewoong Oh
We pose it as a property estimation problem, and study the fundamental trade-offs involved in the accuracy in estimated privacy guarantees and the number of samples required.
Information Theory Information Theory