80 citations · 345 across the 29 of their papers we have counts for
16 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
Recovering the Optimal Solution by Dual Random Projection
Lijun Zhang, Mehrdad Mahdavi, Rong Jin +2
Random projection has been widely used in data classification. It maps high-dimensional data into a low-dimensional subspace in order to reduce the computational cost in solving th…
An Efficient Primal-Dual Prox Method for Non-Smooth Optimization
Tianbao Yang, Mehrdad Mahdavi, Rong Jin +1
We study the non-smooth optimization problems in machine learning, where both the loss function and the regularizer are non-smooth functions. Previous studies on efficient empirica…
Regret Bound by Variation for Online Convex Optimization
Tianbao Yang, Mehrdad Mahdavi, Rong Jin +1
In citep{Hazan-2008-extract}, the authors showed that the regret of online linear optimization can be bounded by the total variation of the cost vectors. In this paper, we extend t…