no code implementations • 1 Sep 2023 • Lingxiao Huang, Jung-Hsuan Wu, Chiching Wei, Wilson Li
The recognition of information in floor plan data requires the use of detection and segmentation models.
no code implementations • 9 Aug 2023 • Zhang-Hua Fu, Sipeng Sun, Jintong Ren, Tianshu Yu, Haoyu Zhang, Yuanyuan Liu, Lingxiao Huang, Xiang Yan, Pinyan Lu
Fair comparisons based on nineteen famous large-scale instances (with 10, 000 to 10, 000, 000 cities) show that HDR is highly competitive against existing state-of-the-art TSP algorithms, in terms of both efficiency and solution quality.
1 code implementation • 16 Jun 2023 • Niclas Boehmer, L. Elisa Celis, Lingxiao Huang, Anay Mehrotra, Nisheeth K. Vishnoi
We consider the problem of subset selection where one is given multiple rankings of items and the goal is to select the highest ``quality'' subset.
1 code implementation • 26 Oct 2022 • Lingxiao Huang, Zhize Li, Jialin Sun, Haoyu Zhao
Vertical federated learning (VFL), where data features are stored in multiple parties distributively, is an important area in machine learning.
no code implementations • 26 Mar 2022 • Sha Yuan, Hanyu Zhao, Shuai Zhao, Jiahong Leng, Yangxiao Liang, Xiaozhi Wang, Jifan Yu, Xin Lv, Zhou Shao, Jiaao He, Yankai Lin, Xu Han, Zhenghao Liu, Ning Ding, Yongming Rao, Yizhao Gao, Liang Zhang, Ming Ding, Cong Fang, Yisen Wang, Mingsheng Long, Jing Zhang, Yinpeng Dong, Tianyu Pang, Peng Cui, Lingxiao Huang, Zheng Liang, HuaWei Shen, HUI ZHANG, Quanshi Zhang, Qingxiu Dong, Zhixing Tan, Mingxuan Wang, Shuo Wang, Long Zhou, Haoran Li, Junwei Bao, Yingwei Pan, Weinan Zhang, Zhou Yu, Rui Yan, Chence Shi, Minghao Xu, Zuobai Zhang, Guoqiang Wang, Xiang Pan, Mengjie Li, Xiaoyu Chu, Zijun Yao, Fangwei Zhu, Shulin Cao, Weicheng Xue, Zixuan Ma, Zhengyan Zhang, Shengding Hu, Yujia Qin, Chaojun Xiao, Zheni Zeng, Ganqu Cui, Weize Chen, Weilin Zhao, Yuan YAO, Peng Li, Wenzhao Zheng, Wenliang Zhao, Ziyi Wang, Borui Zhang, Nanyi Fei, Anwen Hu, Zenan Ling, Haoyang Li, Boxi Cao, Xianpei Han, Weidong Zhan, Baobao Chang, Hao Sun, Jiawen Deng, Chujie Zheng, Juanzi Li, Lei Hou, Xigang Cao, Jidong Zhai, Zhiyuan Liu, Maosong Sun, Jiwen Lu, Zhiwu Lu, Qin Jin, Ruihua Song, Ji-Rong Wen, Zhouchen Lin, LiWei Wang, Hang Su, Jun Zhu, Zhifang Sui, Jiajun Zhang, Yang Liu, Xiaodong He, Minlie Huang, Jian Tang, Jie Tang
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
no code implementations • NeurIPS 2021 • Lingxiao Huang, K. Sudhir, Nisheeth K. Vishnoi
In particular, we consider the setting where the time series data on $N$ entities is generated from a Gaussian mixture model with autocorrelations over $k$ clusters in $\mathbb{R}^d$.
no code implementations • 29 Sep 2021 • Shaofeng Zhang, Meng Liu, Junchi Yan, Hengrui Zhang, Lingxiao Huang, Pinyan Lu, Xiaokang Yang
Negative pairs are essential in contrastive learning, which plays the role of avoiding degenerate solutions.
1 code implementation • 23 Feb 2021 • Shivin Srivastava, Siddharth Bhatia, Lingxiao Huang, Lim Jun Heng, Kenji Kawaguchi, Vaibhav Rajan
In data containing heterogeneous subpopulations, classification performance benefits from incorporating the knowledge of cluster structure in the classifier.
2 code implementations • 16 Dec 2020 • Chang Liu, Zetian Jiang, Runzhong Wang, Junchi Yan, Lingxiao Huang, Pinyan Lu
As such, the agent can finish inlier matching timely when the affinity score stops growing, for which otherwise an additional parameter i. e. the number of inliers is needed to avoid matching outliers.
1 code implementation • NeurIPS 2020 • Lingxiao Huang, K. Sudhir, Nisheeth K. Vishnoi
We first define coresets for several variants of regression problems with panel data and then present efficient algorithms to construct coresets of size that depend polynomially on 1/$\varepsilon$ (where $\varepsilon$ is the error parameter) and the number of regression parameters - independent of the number of individuals in the panel data or the time units each individual is observed for.
1 code implementation • 8 Jun 2020 • L. Elisa Celis, Lingxiao Huang, Vijay Keswani, Nisheeth K. Vishnoi
We present an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes.
1 code implementation • NeurIPS 2019 • Lingxiao Huang, Shaofeng H. -C. Jiang, Nisheeth K. Vishnoi
Our approach is based on novel constructions of coresets: for the $k$-median objective, we construct an $\varepsilon$-coreset of size $O(\Gamma k^2 \varepsilon^{-d})$ where $\Gamma$ is the number of distinct collections of groups that a point may belong to, and for the $k$-means objective, we show how to construct an $\varepsilon$-coreset of size $O(\Gamma k^3\varepsilon^{-d-1})$.
1 code implementation • 21 Feb 2019 • Lingxiao Huang, Nisheeth K. Vishnoi
Theoretically, we prove a stability guarantee, that was lacking in fair classification algorithms, and also provide an accuracy guarantee for our extended framework.
4 code implementations • 15 Jun 2018 • L. Elisa Celis, Lingxiao Huang, Vijay Keswani, Nisheeth K. Vishnoi
The main contribution of this paper is a new meta-algorithm for classification that takes as input a large class of fairness constraints, with respect to multiple non-disjoint sensitive attributes, and which comes with provable guarantees.
1 code implementation • 27 Oct 2017 • L. Elisa Celis, Lingxiao Huang, Nisheeth K. Vishnoi
Multiwinner voting rules are used to select a small representative subset of candidates or items from a larger set given the preferences of voters.
no code implementations • 20 May 2017 • Yifei Jin, Lingxiao Huang, Jian Li
Our algorithms achieve $(1-\epsilon)$-approximations with running time $\tilde{O}(nd+n\sqrt{d / \epsilon})$ for both variants, where $n$ is the number of points and $d$ is the dimensionality.