Supervised Machine Learning with a Novel Kernel Density Estimator

Oct 16, 2007

Yen-Jen Oyang, Darby Tien-Hao Chang, Yu-Yen Ou, Hao-Geng Hung, Chih-Peng Wu, Chien-Yu Chen

In recent years, kernel density estimation has been exploited by computer scientists to model machine learning problems. The kernel density estimation based approaches are of interest due to the low time complexity of either O(n) or O(n*log(n)) for constructing a classifier, where n is the number of sampling instances. Concerning design of kernel density estimators, one essential issue is how fast the pointwise mean square error (MSE) and/or the integrated mean square error (IMSE) diminish as the number of sampling instances increases. In this article, it is shown that with the proposed kernel function it is feasible to make the pointwise MSE of the density estimator converge at O(n^-2/3) regardless of the dimension of the vector space, provided that the probability density function at the point of interest meets certain conditions.
Oct 16, 2007

Yen-Jen Oyang, Darby Tien-Hao Chang, Yu-Yen Ou, Hao-Geng Hung, Chih-Peng Wu, Chien-Yu Chen

* The new version includes an additional theorem, Theorem 3

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Complement Objective Training

Mar 21, 2019

Hao-Yun Chen, Pei-Hsin Wang, Chun-Hao Liu, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan

Mar 21, 2019

Hao-Yun Chen, Pei-Hsin Wang, Chun-Hao Liu, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan

* ICLR'19 Camera Ready

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Improving Adversarial Robustness via Guided Complement Entropy

Mar 23, 2019

Hao-Yun Chen, Jhao-Hong Liang, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan

Mar 23, 2019

Hao-Yun Chen, Jhao-Hong Liang, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan

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Dixit: Interactive Visual Storytelling via Term Manipulation

Mar 11, 2019

Chao-Chun Hsu, Yu-Hua Chen, Zi-Yuan Chen, Hsin-Yu Lin, Ting-Hao 'Kenneth' Huang, Lun-Wei Ku

Mar 11, 2019

Chao-Chun Hsu, Yu-Hua Chen, Zi-Yuan Chen, Hsin-Yu Lin, Ting-Hao 'Kenneth' Huang, Lun-Wei Ku

* WWW'19 Demo, demo video: https://www.youtube.com/watch?v=CUu1MOwnveI

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Comfort-Centered Design of a Lightweight and Backdrivable Knee Exoskeleton

Feb 11, 2019

Junlin Wang, Xiao Li, Tzu-Hao Huang, Shuangyue Yu, Yanjun Li, Tianyao Chen, Alessandra Carriero, Mooyeon Oh-Park, Hao Su*, Member, IEEE

Feb 11, 2019

Junlin Wang, Xiao Li, Tzu-Hao Huang, Shuangyue Yu, Yanjun Li, Tianyao Chen, Alessandra Carriero, Mooyeon Oh-Park, Hao Su*, Member, IEEE

* 8 pages, 16figures, Journal

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A Soft High Force Hand Exoskeleton for Rehabilitation and Assistance of Spinal Cord Injury and Stroke Individuals

Feb 19, 2019

Shuangyue Yu, Hadia Perez, James Barkas, Mohamed Mohamed, Mohamed Eldaly, Tzu-Hao Huang, Xiaolong Yang, Hao Su, Maria del Mar Cortes, Dylan J. Edwards

Feb 19, 2019

Shuangyue Yu, Hadia Perez, James Barkas, Mohamed Mohamed, Mohamed Eldaly, Tzu-Hao Huang, Xiaolong Yang, Hao Su, Maria del Mar Cortes, Dylan J. Edwards

* 5 pages, 5 figures

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Design and Control of a Quasi-Direct Drive Soft Hybrid Knee Exoskeleton for Injury Prevention during Squatting

Feb 19, 2019

Shuangyue Yu, Tzu-Hao Huang, Dianpeng Wang, Brian Lynn, Dina Sayd, Viktor Silivanov, Young Soo Park, Yingli Tian, Fellow, IEEE, Hao Su, Member, IEEE

Feb 19, 2019

Shuangyue Yu, Tzu-Hao Huang, Dianpeng Wang, Brian Lynn, Dina Sayd, Viktor Silivanov, Young Soo Park, Yingli Tian, Fellow, IEEE, Hao Su, Member, IEEE

* 8 pages, 14 figures

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On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization

May 10, 2019

Hao Yu, Rong Jin

May 10, 2019

Hao Yu, Rong Jin

* A short version is accepted to ICML 2019

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A Low Complexity Algorithm with $O(\sqrt{T})$ Regret and Finite Constraint Violations for Online Convex Optimization with Long Term Constraints

Oct 05, 2016

Hao Yu, Michael J. Neely

Oct 05, 2016

Hao Yu, Michael J. Neely

* In the previous version, both the regret bound and the constraint violation bound are $O(\sqrt{T})$. The current version improves the constraint violation bound from $O(\sqrt{T})$ to $O(1)$, i.e., a finite constant that is independent of T, while preserving the same $O(\sqrt{T})$ regret bound

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On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization

May 09, 2019

Hao Yu, Rong Jin, Sen Yang

May 09, 2019

Hao Yu, Rong Jin, Sen Yang

* A short version of this paper is accepted to ICML 2019

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Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout

Apr 08, 2019

Hao Tan, Licheng Yu, Mohit Bansal

Apr 08, 2019

Hao Tan, Licheng Yu, Mohit Bansal

* NAACL 2019 (12 pages)

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Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers

Jan 10, 2019

Daniel Liu, Ronald Yu, Hao Su

Jan 10, 2019

Daniel Liu, Ronald Yu, Hao Su

* 8 pages, 3 figures, 5 tables

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MOHONE: Modeling Higher Order Network Effects in KnowledgeGraphs via Network Infused Embeddings

Nov 01, 2018

Hao Yu, Vivek Kulkarni, William Wang

Nov 01, 2018

Hao Yu, Vivek Kulkarni, William Wang

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Parallel Restarted SGD for Non-Convex Optimization with Faster Convergence and Less Communication

Jul 17, 2018

Hao Yu, Sen Yang, Shenghuo Zhu

Jul 17, 2018

Hao Yu, Sen Yang, Shenghuo Zhu

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Orthogonal Echo State Networks and stochastic evaluations of likelihoods

Jun 13, 2017

Norbert Michael Mayer, Ying-Hao Yu

Jun 13, 2017

Norbert Michael Mayer, Ying-Hao Yu

* Cogn Comput (2017) 9:379-390

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Occlusion-Model Guided Anti-Occlusion Depth Estimation in Light Field

Aug 18, 2016

Hao Zhu, Qing Wang, Jingyi Yu

Aug 18, 2016

Hao Zhu, Qing Wang, Jingyi Yu

* 19 pages, 13 figures, pdflatex

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Detail Preserving Depth Estimation from a Single Image Using Attention Guided Networks

Sep 03, 2018

Zhixiang Hao, Yu Li, Shaodi You, Feng Lu

Sep 03, 2018

Zhixiang Hao, Yu Li, Shaodi You, Feng Lu

* Published at IEEE International Conference on 3D Vision (3DV) 2018

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LSTD: A Low-Shot Transfer Detector for Object Detection

Mar 05, 2018

Hao Chen, Yali Wang, Guoyou Wang, Yu Qiao

Mar 05, 2018

Hao Chen, Yali Wang, Guoyou Wang, Yu Qiao

* Accepted by AAAI2018

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Online Convex Optimization with Stochastic Constraints

Aug 12, 2017

Hao Yu, Michael J. Neely, Xiaohan Wei

This paper considers online convex optimization (OCO) with stochastic constraints, which generalizes Zinkevich's OCO over a known simple fixed set by introducing multiple stochastic functional constraints that are i.i.d. generated at each round and are disclosed to the decision maker only after the decision is made. This formulation arises naturally when decisions are restricted by stochastic environments or deterministic environments with noisy observations. It also includes many important problems as special cases, such as OCO with long term constraints, stochastic constrained convex optimization, and deterministic constrained convex optimization. To solve this problem, this paper proposes a new algorithm that achieves $O(\sqrt{T})$ expected regret and constraint violations and $O(\sqrt{T}\log(T))$ high probability regret and constraint violations. Experiments on a real-world data center scheduling problem further verify the performance of the new algorithm.
Aug 12, 2017

Hao Yu, Michael J. Neely, Xiaohan Wei

* This paper extends our own ArXiv reports arXiv:1604.02218 (by considering more general stochastic functional constraints) and arXiv:1702.04783 (by relaxing a deterministic Slater-type assumption to a weaker stochastic Slater assumption; refining proofs; and providing high probability performance guarantees). See Introduction section (especially footnotes 1 and 2) for more details of distinctions

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