57 citations · 157 across the 32 of their papers we have counts for
33 papers
MuraNet: Multi-task Floor Plan Recognition with Relation Attention
Lingxiao Huang, Jung-Hsuan Wu, Chiching Wei +1
The recognition of information in floor plan data requires the use of detection and segmentation models. However, relying on several single-task models can result in ineffective ut…
A Hierarchical Destroy and Repair Approach for Solving Very Large-Scale Travelling Salesman Problem
Zhang-Hua Fu, Sipeng Sun, Jintong Ren +6
For prohibitively large-scale Travelling Salesman Problems (TSPs), existing algorithms face big challenges in terms of both computational efficiency and solution quality. To addres…
Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions
Niclas Boehmer, L. Elisa Celis, Lingxiao Huang +2
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. Score functions from the multiwi…
On Coresets for Clustering in Small Dimensional Euclidean Spaces
Lingxiao Huang, Ruiyuan Huang, Zengfeng Huang +1
We consider the problem of constructing small coresets for -Median in Euclidean spaces. Given a large set of data points , a coreset is a much smaller set…
The Power of Uniform Sampling for -Median
Lingxiao Huang, Shaofeng H. -C. Jiang, Jianing Lou
We study the power of uniform sampling for -Median in various metric spaces. We relate the query complexity for approximating -Median, to a key parameter of the dataset, call…
Improved Coresets for Clustering with Capacity and Fairness Constraints
Lingxiao Huang, Pinyan Lu, Xuan Wu
We study coresets for clustering with capacity and fairness constraints. Our main result is a near-linear time algorithm to construct -sized $\va…
Coresets for Constrained Clustering: General Assignment Constraints and Improved Size Bounds
Lingxiao Huang, Jian Li, Pinyan Lu +1
Designing small-sized \emph{coresets}, which approximately preserve the costs of the solutions for large datasets, has been an important research direction for the past decade. We…
On Optimal Coreset Construction for Euclidean -Clustering
Lingxiao Huang, Jian Li, Xuan Wu
Constructing small-sized coresets for various clustering problems in different metric spaces has attracted significant attention for the past decade. A central problem in the cores…
Coresets for Vertical Federated Learning: Regularized Linear Regression and -Means Clustering
Lingxiao Huang, Zhize Li, Jialin Sun +1
Vertical federated learning (VFL), where data features are stored in multiple parties distributively, is an important area in machine learning. However, the communication complexit…
Efficient Submodular Optimization under Noise: Local Search is Robust
Lingxiao Huang, Yuyi Wang, Chunxue Yang +1
The problem of monotone submodular maximization has been studied extensively due to its wide range of applications. However, there are cases where one can only access the objective…
Near-optimal Coresets for Robust Clustering
Lingxiao Huang, Shaofeng H. -C. Jiang, Jianing Lou +1
We consider robust clustering problems in , specifically -clustering problems (e.g., -Median and -Means with outliers, where the cost for a given center…
A Roadmap for Big Model
Sha Yuan, Hanyu Zhao, Shuai Zhao +97
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the c…
Coresets for Time Series Clustering
Lingxiao Huang, K. Sudhir, Nisheeth K. Vishnoi
We study the problem of constructing coresets for clustering problems with time series data. This problem has gained importance across many fields including biology, medicine, and…
Clustering Aware Classification for Risk Prediction and Subtyping in Clinical Data
Shivin Srivastava, Siddharth Bhatia, Lingxiao Huang +3
In data containing heterogeneous subpopulations, classification performance benefits from incorporating the knowledge of cluster structure in the classifier. Previous methods for s…
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching
Chang Liu, Zetian Jiang, Runzhong Wang +3
Graph matching (GM) has been a building block in various areas including computer vision and pattern recognition. Despite recent impressive progress, existing deep GM methods often…
Coresets for Regressions with Panel Data
Lingxiao Huang, K. Sudhir, Nisheeth K. Vishnoi
This paper introduces the problem of coresets for regression problems to panel data settings. We first define coresets for several variants of regression problems with panel data a…
Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees
L. Elisa Celis, Lingxiao Huang, Vijay Keswani +1
We present an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes. Compared to prior work, our framework can be…
Coresets for Clustering in Euclidean Spaces: Importance Sampling is Nearly Optimal
Lingxiao Huang, Nisheeth K. Vishnoi
Given a collection of points in , the goal of the -clustering problem is to find a subset of "centers" that minimizes the sum of the -th powers of t…
Coresets for Clustering in Graphs of Bounded Treewidth
Daniel Baker, Vladimir Braverman, Lingxiao Huang +3
We initiate the study of coresets for clustering in graph metrics, i.e., the shortest-path metric of edge-weighted graphs. Such clustering problems are essential to data analysis a…
Coresets for Clustering with Fairness Constraints
Lingxiao Huang, Shaofeng H. -C. Jiang, Nisheeth K. Vishnoi
In a recent work, [19] studied the following "fair" variants of classical clustering problems such as -means and -median: given a set of data points in and…
Stable and Fair Classification
Lingxiao Huang, Nisheeth K. Vishnoi
Fair classification has been a topic of intense study in machine learning, and several algorithms have been proposed towards this important task. However, in a recent study, Friedl…
Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees
L. Elisa Celis, Lingxiao Huang, Vijay Keswani +1
Developing classification algorithms that are fair with respect to sensitive attributes of the data has become an important problem due to the growing deployment of classification…
-Coresets for Clustering (with Outliers) in Doubling Metrics
Lingxiao Huang, Shaofeng H. -C. Jiang, Jian Li +1
We study the problem of constructing -coresets for the -clustering problem in a doubling metric . An -coreset is a weighted subset $S\sub…
Multiwinner Voting with Fairness Constraints
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. However, if candidates have sens…