Search Results for author: Hu Ding

Found 15 papers, 0 papers with code

Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problems

no code implementations NeurIPS 2021 Zixiu Wang, Yiwen Guo, Hu Ding

In this paper, we propose a novel robust coreset method for the {\em continuous-and-bounded learning} problems (with outliers) which includes a broad range of popular optimization objectives in machine learning, {\em e. g.,} logistic regression and $ k $-means clustering.

Robust Coreset for Continuous-and-Bounded Learning (with Outliers)

no code implementations30 Jun 2021 Zixiu Wang, Yiwen Guo, Hu Ding

In this paper, we propose a novel robust coreset method for the {\em continuous-and-bounded learning} problem (with outliers) which includes a broad range of popular optimization objectives in machine learning, like logistic regression and $ k $-means clustering.

Is Simple Uniform Sampling Efficient for Center-Based Clustering With Outliers: When and Why?

no code implementations28 Feb 2021 Hu Ding, Jiawei Huang

In this paper, we propose a framework for solving three representative center-based clustering with outliers problems: $k$-center/median/means clustering with outliers.

Gradient Episodic Memory with a Soft Constraint for Continual Learning

no code implementations16 Nov 2020 Guannan Hu, Wu Zhang, Hu Ding, Wenhao Zhu

Catastrophic forgetting in continual learning is a common destructive phenomenon in gradient-based neural networks that learn sequential tasks, and it is much different from forgetting in humans, who can learn and accumulate knowledge throughout their whole lives.

Continual Learning

Defending SVMs against Poisoning Attacks: the Hardness and DBSCAN Approach

no code implementations14 Jun 2020 Hu Ding, Fan Yang, Jiawei Huang

For the data sanitization defense, we link it to the intrinsic dimensionality of data; in particular, we provide a sampling theorem in doubling metrics for explaining the effectiveness of DBSCAN (as a density-based outlier removal method) for defending against poisoning attacks.

The Effectiveness of Uniform Sampling for Center-Based Clustering with Outliers

no code implementations24 May 2019 Hu Ding, Jiawei Huang, Haikuo Yu

The experiments suggest that the uniform sampling method can achieve comparable clustering results with other existing methods, but greatly reduce the running times.

Minimum Enclosing Ball Revisited: Stability and Sub-linear Time Algorithms

no code implementations8 Apr 2019 Hu Ding

Though the problem has been extensively studied before, most of the existing algorithms need at least linear time (in the number of input points $n$ and the dimensionality $d$) to achieve a $(1+\epsilon)$-approximation.

Greedy Strategy Works for $k$-Center Clustering with Outliers and Coreset Construction

no code implementations24 Jan 2019 Hu Ding, Haikuo Yu, Zixiu Wang

Our idea is inspired by the greedy method, Gonzalez's algorithm, for solving the problem of ordinary $k$-center clustering.

On Geometric Alignment in Low Doubling Dimension

no code implementations19 Nov 2018 Hu Ding, Mingquan Ye

In real-world, many problems can be formulated as the alignment between two geometric patterns.

A Unified Framework for Clustering Constrained Data without Locality Property

no code implementations2 Oct 2018 Hu Ding, Jinhui Xu

To overcome the difficulty caused by the loss of locality, we present in this paper a unified framework, called {\em Peeling-and-Enclosing (PnE)}, to iteratively solve two variants of the constrained clustering problems, {\em constrained $k$-means clustering} ($k$-CMeans) and {\em constrained $k$-median clustering} ($k$-CMedian).

Faster Balanced Clusterings in High Dimension

no code implementations4 Sep 2018 Hu Ding

For the balanced $k$-center clustering, we provide a $4$-approximation algorithm that improves the existing approximation factors; for the balanced $k$-median and $k$-means clusterings, our algorithms yield constant and $(1+\epsilon)$-approximation factors with any $\epsilon>0$.

k-Prototype Learning for 3D Rigid Structures

no code implementations NeurIPS 2013 Hu Ding, Ronald Berezney, Jinhui Xu

In this paper, we study the following new variant of prototype learning, called {\em $k$-prototype learning problem for 3D rigid structures}: Given a set of 3D rigid structures, find a set of $k$ rigid structures so that each of them is a prototype for a cluster of the given rigid structures and the total cost (or dissimilarity) is minimized.

Gauging Association Patterns of Chromosome Territories via Chromatic Median

no code implementations CVPR 2013 Hu Ding, Branislav Stojkovic, Ronald Berezney, Jinhui Xu

In this paper, we introduce a novel algorithmic tool for investigating association patterns of chromosome territories in a population of cells.

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