no code implementations • 15 Apr 2024 • Zhenwei Huang, Wen Huang, Pratik Jawanpuria, Bamdev Mishra
To the best of our knowledge, this is the first federated learning framework on Riemannian manifold with a privacy guarantee and convergence results.
1 code implementation • 22 Feb 2024 • Wen Huang, Hongbin Liu, Minxin Guo, Neil Zhenqiang Gong
We find that existing MLLMs such as GPT-4V, LLaVA-1. 5, and MiniGPT-v2 hallucinate for a large fraction of the instances in our benchmark.
no code implementations • 20 Dec 2023 • Wen Huang, Xintao Wu
A major obstacle in this setting is the existence of compound biases from the observational data.
1 code implementation • 10 Dec 2023 • Xiaojian Yuan, Kejiang Chen, Wen Huang, Jie Zhang, Weiming Zhang, Nenghai Yu
In response to these identified gaps, we introduce a novel Data-Free Hard-Label Robustness Stealing (DFHL-RS) attack in this paper, which enables the stealing of both model accuracy and robustness by simply querying hard labels of the target model without the help of any natural data.
1 code implementation • 15 Sep 2023 • Karuna Bhaila, Wen Huang, Yongkai Wu, Xintao Wu
We focus on a decentralized notion of Differential Privacy, namely Local Differential Privacy, and apply randomization mechanisms to perturb both feature and label data at the node level before the data is collected by a central server for model training.
no code implementations • 25 May 2023 • Huy Mai, Wen Huang, Wei Du, Xintao Wu
In this paper, we propose BiasCorr, an algorithm that improves on Greene's method by modifying the original training set in order for a classifier to learn under MNAR sample selection bias.
no code implementations • 21 Sep 2021 • Wen Huang, Lu Zhang, Xintao Wu
In online recommendation, customers arrive in a sequential and stochastic manner from an underlying distribution and the online decision model recommends a chosen item for each arriving individual based on some strategy.
no code implementations • 17 Nov 2020 • Yuetian Luo, Wen Huang, Xudong Li, Anru R. Zhang
In this paper, we propose {\it \underline{R}ecursive} {\it \underline{I}mportance} {\it \underline{S}ketching} algorithm for {\it \underline{R}ank} constrained least squares {\it \underline{O}ptimization} (RISRO).
no code implementations • 22 Oct 2020 • Wen Huang, Kevin Labille, Xintao Wu, Dongwon Lee, Neil Heffernan
Personalized recommendation based on multi-arm bandit (MAB) algorithms has shown to lead to high utility and efficiency as it can dynamically adapt the recommendation strategy based on feedback.
no code implementations • 11 Nov 2019 • Wen Huang, Yongkai Wu, Lu Zhang, Xintao Wu
We develop algorithms for determining whether an individual or a group of individuals is discriminated in terms of equality of effort.
no code implementations • ICLR 2019 • Reinhard Heckel, Wen Huang, Paul Hand, Vladislav Voroninski
Deep neural networks provide state-of-the-art performance for image denoising, where the goal is to recover a near noise-free image from a noisy image.
no code implementations • ICLR 2019 • Reinhard Heckel, Wen Huang, Paul Hand, Vladislav Voroninski
Deep neural networks provide state-of-the-art performance for image denoising, where the goal is to recover a near noise-free image from a noisy observation.