80 citations · 345 across the 29 of their papers we have counts for
30 papers
Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement
Ming Lin, Xiaomin Song, Qi Qian +4
Satellite-based positioning system such as GPS often suffers from large amount of noise that degrades the positioning accuracy dramatically especially in real-time applications. In…
Which Factorization Machine Modeling is Better: A Theoretical Answer with Optimal Guarantee
Ming Lin, Shuang Qiu, Jieping Ye +5
Factorization machine (FM) is a popular machine learning model to capture the second order feature interactions. The optimal learning guarantee of FM and its generalized version is…
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
Qingsong Wen, Jingkun Gao, Xiaomin Song +3
Decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting. Although numerou…
Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning
Hao Yu, Sen Yang, Shenghuo Zhu
In distributed training of deep neural networks, parallel mini-batch SGD is widely used to speed up the training process by using multiple workers. It uses multiple workers to samp…
Large-scale Distance Metric Learning with Uncertainty
Qi Qian, Jiasheng Tang, Hao Li +2
Distance metric learning (DML) has been studied extensively in the past decades for its superior performance with distance-based algorithms. Most of the existing methods propose to…
Learning with Non-Convex Truncated Losses by SGD
Yi Xu, Shenghuo Zhu, Sen Yang +3
Learning with a {\it convex loss} function has been a dominating paradigm for many years. It remains an interesting question how non-convex loss functions help improve the generali…
Robust Optimization over Multiple Domains
Qi Qian, Shenghuo Zhu, Jiasheng Tang +3
In this work, we study the problem of learning a single model for multiple domains. Unlike the conventional machine learning scenario where each domain can have the corresponding m…
Multinomial Logit Bandit with Linear Utility Functions
Mingdong Ou, Nan Li, Shenghuo Zhu +1
Multinomial logit bandit is a sequential subset selection problem which arises in many applications. In each round, the player selects a -cardinality subset from candidate i…
Adaptive Recurrent Neural Network Based on Mixture Layer
Kui Zhao, Yuechuan Li, Chi Zhang +2
Although Recurrent Neural Network (RNN) has been a powerful tool for modeling sequential data, its performance is inadequate when processing sequences with multiple patterns. In th…
Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM
Cong Leng, Hao Li, Shenghuo Zhu +1
Although deep learning models are highly effective for various learning tasks, their high computational costs prohibit the deployment to scenarios where either memory or computatio…
Similarity Learning via Adaptive Regression and Its Application to Image Retrieval
Qi Qian, Inci M. Baytas, Rong Jin +2
We study the problem of similarity learning and its application to image retrieval with large-scale data. The similarity between pairs of images can be measured by the distances be…
Towards Making High Dimensional Distance Metric Learning Practical
Qi Qian, Rong Jin, Lijun Zhang +1
In this work, we study distance metric learning (DML) for high dimensional data. A typical approach for DML with high dimensional data is to perform the dimensionality reduction fi…
An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection
Tianbao Yang, Lijun Zhang, Rong Jin +1
In this paper, we consider the problem of column subset selection. We present a novel analysis of the spectral norm reconstruction for a simple randomized algorithm and establish a…
Theory of Dual-sparse Regularized Randomized Reduction
Tianbao Yang, Lijun Zhang, Rong Jin +1
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 hashi…
A Simple Homotopy Proximal Mapping for Compressive Sensing
Tianbao Yang, Lijun Zhang, Rong Jin +2
In this paper, we present a novel yet simple homotopy proximal mapping algorithm for compressive sensing. The algorithm adopts a simple proximal mapping of the norm at eac…
On Data Preconditioning for Regularized Loss Minimization
Tianbao Yang, Rong Jin, Shenghuo Zhu +1
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. It is…
CUR Algorithm with Incomplete Matrix Observation
Rong Jin, Shenghuo Zhu
CUR matrix decomposition is a randomized algorithm that can efficiently compute the low rank approximation for a given rectangle matrix. One limitation with the existing CUR algori…
Fine-Grained Visual Categorization via Multi-stage Metric Learning
Qi Qian, Rong Jin, Shenghuo Zhu +1
Fine-grained visual categorization (FGVC) is to categorize objects into subordinate classes instead of basic classes. One major challenge in FGVC is the co-occurrence of two issues…
Analysis of Distributed Stochastic Dual Coordinate Ascent
Tianbao Yang, Shenghuo Zhu, Rong Jin +1
In \citep{Yangnips13}, the author presented distributed stochastic dual coordinate ascent (DisDCA) algorithms for solving large-scale regularized loss minimization. Extraordinary p…
One-Pass AUC Optimization
Wei Gao, Rong Jin, Shenghuo Zhu +1
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. In this work…
Stochastic gradient descent algorithms for strongly convex functions at O(1/T) convergence rates
Shenghuo Zhu
With a weighting scheme proportional to t, a traditional stochastic gradient descent (SGD) algorithm achieves a high probability convergence rate of O(κ/T) for strongly convex func…
A New Analysis of Compressive Sensing by Stochastic Proximal Gradient Descent
Rong Jin, Tianbao Yang, Shenghuo Zhu
In this manuscript, we analyze the sparse signal recovery (compressive sensing) problem from the perspective of convex optimization by stochastic proximal gradient descent. This vi…
Efficient Distance Metric Learning by Adaptive Sampling and Mini-Batch Stochastic Gradient Descent (SGD)
Qi Qian, Rong Jin, Jinfeng Yi +2
Distance metric learning (DML) is an important task that has found applications in many domains. The high computational cost of DML arises from the large number of variables to be…
Large Scale Strongly Supervised Ensemble Metric Learning, with Applications to Face Verification and Retrieval
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- putationa…