no code implementations • 3 Jun 2019 • Ming Lin, Xiaomin Song, Qi Qian, Hao Li, Liang Sun, Shenghuo Zhu, Rong Jin
We validate the superiority of the proposed method in our real-time high precision positioning system against several popular state-of-the-art robust regression methods.
no code implementations • 30 Jan 2019 • Ming Lin, Shuang Qiu, Jieping Ye, Xiaomin Song, Qi Qian, Liang Sun, Shenghuo Zhu, Rong Jin
This bound is sub-optimal comparing to the information theoretical lower bound $\mathcal{O}(kd)$.
1 code implementation • 5 Dec 2018 • Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, Huan Xu, Shenghuo Zhu
Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component.
no code implementations • 17 Jul 2018 • Hao Yu, Sen yang, Shenghuo Zhu
Ideally, parallel mini-batch SGD can achieve a linear speed-up of the training time (with respect to the number of workers) compared with SGD over a single worker.
no code implementations • CVPR 2018 • Qi Qian, Jiasheng Tang, Hao Li, Shenghuo Zhu, Rong Jin
Furthermore, we can show that the metric is learned from latent examples only, but it can preserve the large margin property even for the original data.
no code implementations • 21 May 2018 • Yi Xu, Shenghuo Zhu, Sen yang, Chi Zhang, Rong Jin, Tianbao Yang
Learning with a {\it convex loss} function has been a dominating paradigm for many years.
no code implementations • 19 May 2018 • Qi Qian, Shenghuo Zhu, Jiasheng Tang, Rong Jin, Baigui Sun, Hao Li
Hence, we propose to learn the model and the adversarial distribution simultaneously with the stochastic algorithm for efficiency.
no code implementations • 8 May 2018 • Mingdong Ou, Nan Li, Shenghuo Zhu, Rong Jin
In each round, the player selects a $K$-cardinality subset from $N$ candidate items, and receives a reward which is governed by a {\it multinomial logit} (MNL) choice model considering both item utility and substitution property among items.
no code implementations • 24 Jul 2017 • Cong Leng, Hao Li, Shenghuo Zhu, Rong Jin
Although deep learning models are highly effective for various learning tasks, their high computational costs prohibit the deployment to scenarios where either memory or computational resources are limited.
no code implementations • 6 Dec 2015 • Qi Qian, Inci M. Baytas, Rong Jin, Anil Jain, Shenghuo Zhu
The similarity between pairs of images can be measured by the distances between their high dimensional representations, and the problem of learning the appropriate similarity is often addressed by distance metric learning.
no code implementations • 15 Sep 2015 • Qi Qian, Rong Jin, Lijun Zhang, Shenghuo Zhu
In this work, we present a dual random projection frame for DML with high dimensional data that explicitly addresses the limitation of dimensionality reduction for DML.
no code implementations • 4 May 2015 • Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu
In this paper, we consider the problem of column subset selection.
no code implementations • 15 Apr 2015 • Tianbao Yang, Lijun Zhang, Rong Jin, Shenghuo Zhu
In this paper, we study randomized reduction methods, which reduce high-dimensional features into low-dimensional space by randomized methods (e. g., random projection, random hashing), for large-scale high-dimensional classification.
no code implementations • 13 Aug 2014 • Tianbao Yang, Rong Jin, Shenghuo Zhu, Qihang Lin
In this work, we study data preconditioning, a well-known and long-existing technique, for boosting the convergence of first-order methods for regularized loss minimization.
no code implementations • 22 Mar 2014 • Rong Jin, Shenghuo Zhu
Our goal is to develop a low rank approximation algorithm, similar to CUR, based on (i) randomly sampled rows and columns from A, and (ii) randomly sampled entries from A.
no code implementations • CVPR 2015 • Qi Qian, Rong Jin, Shenghuo Zhu, Yuanqing Lin
To this end, we proposed a multi-stage metric learning framework that divides the large-scale high dimensional learning problem to a series of simple subproblems, achieving $\mathcal{O}(d)$ computational complexity.
no code implementations • 4 Dec 2013 • Tianbao Yang, Shenghuo Zhu, Rong Jin, Yuanqing Lin
Extraordinary performances have been observed and reported for the well-motivated updates, as referred to the practical updates, compared to the naive updates.
no code implementations • CVPR 2013 • Anelia Angelova, Shenghuo Zhu
The algorithm first detects low-level regions that could potentially belong to the object and then performs a full-object segmentation through propagation.
no code implementations • 9 May 2013 • Shenghuo Zhu
With a weighting scheme proportional to t, a traditional stochastic gradient descent (SGD) algorithm achieves a high probability convergence rate of O({\kappa}/T) for strongly convex functions, instead of O({\kappa} ln(T)/T).
no code implementations • 7 May 2013 • Wei Gao, Rong Jin, Shenghuo Zhu, Zhi-Hua Zhou
AUC is an important performance measure and many algorithms have been devoted to AUC optimization, mostly by minimizing a surrogate convex loss on a training data set.
no code implementations • 3 Apr 2013 • Qi Qian, Rong Jin, Jin-Feng Yi, Lijun Zhang, Shenghuo Zhu
Although stochastic gradient descent (SGD) has been successfully applied to improve the efficiency of DML, it can still be computationally expensive because in order to ensure that the solution is a PSD matrix, it has to, at every iteration, project the updated distance metric onto the PSD cone, an expensive operation.
1 code implementation • 25 Dec 2012 • Chang Huang, Shenghuo Zhu, Kai Yu
Learning Mahanalobis distance metrics in a high- dimensional feature space is very difficult especially when structural sparsity and low rank are enforced to improve com- putational efficiency in testing phase.
no code implementations • NeurIPS 2012 • Mehrdad Mahdavi, Tianbao Yang, Rong Jin, Shenghuo Zhu, Jin-Feng Yi
Although many variants of stochastic gradient descent have been proposed for large-scale convex optimization, most of them require projecting the solution at {\it each} iteration to ensure that the obtained solution stays within the feasible domain.
no code implementations • 13 Nov 2012 • Lijun Zhang, Mehrdad Mahdavi, Rong Jin, Tianbao Yang, Shenghuo Zhu
Random projection has been widely used in data classification.
no code implementations • 24 Jan 2012 • Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Shenghuo Zhu
We study the non-smooth optimization problems in machine learning, where both the loss function and the regularizer are non-smooth functions.
no code implementations • NeurIPS 2010 • Yuanqing Lin, Tong Zhang, Shenghuo Zhu, Kai Yu
This paper proposes a principled extension of the traditional single-layer flat sparse coding scheme, where a two-layer coding scheme is derived based on theoretical analysis of nonlinear functional approximation that extends recent results for local coordinate coding.
no code implementations • NeurIPS 2008 • Shenghuo Zhu, Kai Yu, Yihong Gong
Stochastic relational models provide a rich family of choices for learning and predicting dyadic data between two sets of entities.
no code implementations • NeurIPS 2007 • Shenghuo Zhu, Kai Yu, Yihong Gong
It is becoming increasingly important to learn from a partially-observed random matrix and predict its missing elements.