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Pinghua Gong

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7 papers

2 author rows

AAAI Conference 2018 Conference Paper

Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction

Huaxiu Yao
Fei Wu
Jintao Ke
Xianfeng Tang
Yitian Jia
Siyu Lu
Pinghua Gong
Jieping Ye

Taxi demand prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the city pre-allocate resources to meet travel demand and to reduce empty taxis on streets which waste energy and worsen the trafﬁc congestion. With the increasing popularity of taxi requesting services such as Uber and Didi Chuxing (in China), we are able to collect large-scale taxi demand data continuously. How to utilize such big data to improve the demand prediction is an interesting and critical real-world problem. Traditional demand prediction methods mostly rely on time series forecasting techniques, which fail to model the complex non-linear spatial and temporal relations. Recent advances in deep learning have shown superior performance on traditionally challenging tasks such as image classiﬁcation by learning the complex features and correlations from largescale data. This breakthrough has inspired researchers to explore deep learning techniques on trafﬁc prediction problems. However, existing methods on trafﬁc prediction have only considered spatial relation (e. g. , using CNN) or temporal relation (e. g. , using LSTM) independently. We propose a Deep Multi-View Spatial-Temporal Network (DMVST-Net) framework to model both spatial and temporal relations. Specifically, our proposed model consists of three views: temporal view (modeling correlations between future demand values with near time points via LSTM), spatial view (modeling local spatial correlation via local CNN), and semantic view (modeling correlations among regions sharing similar temporal patterns). Experiments on large-scale real taxi demand data demonstrate effectiveness of our approach over state-ofthe-art methods.

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ICML Conference 2015 Conference Paper

A Modified Orthant-Wise Limited Memory Quasi-Newton Method with Convergence Analysis

Pinghua Gong
Jieping Ye

The Orthant-Wise Limited memory Quasi-Newton (OWL-QN) method has been demonstrated to be very effective in solving the \ell_1-regularized sparse learning problem. OWL-QN extends the L-BFGS from solving unconstrained smooth optimization problems to \ell_1-regularized (non-smooth) sparse learning problems. At each iteration, OWL-QN does not involve any \ell_1-regularized quadratic optimization subproblem and only requires matrix-vector multiplications without an explicit use of the (inverse) Hessian matrix, which enables OWL-QN to tackle large-scale problems efficiently. Although many empirical studies have demonstrated that OWL-QN works quite well in practice, several recent papers point out that the existing convergence proof of OWL-QN is flawed and a rigorous convergence analysis for OWL-QN still remains to be established. In this paper, we propose a modified Orthant-Wise Limited memory Quasi-Newton (mOWL-QN) algorithm by slightly modifying the OWL-QN algorithm. As the main technical contribution of this paper, we establish a rigorous convergence proof for the mOWL-QN algorithm. To the best of our knowledge, our work fills the theoretical gap by providing the first rigorous convergence proof for the OWL-QN-type algorithm on solving \ell_1-regularized sparse learning problems. We also provide empirical studies to show that mOWL-QN works well and is as efficient as OWL-QN.

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NeurIPS Conference 2015 Conference Paper

HONOR: Hybrid Optimization for NOn-convex Regularized problems

Pinghua Gong
Jieping Ye

Recent years have witnessed the superiority of non-convex sparse learning formulations over their convex counterparts in both theory and practice. However, due to the non-convexity and non-smoothness of the regularizer, how to efficiently solve the non-convex optimization problem for large-scale data is still quite challenging. In this paper, we propose an efficient \underline{H}ybrid \underline{O}ptimization algorithm for \underline{NO}n convex \underline{R}egularized problems (HONOR). Specifically, we develop a hybrid scheme which effectively integrates a Quasi-Newton (QN) step and a Gradient Descent (GD) step. Our contributions are as follows: (1) HONOR incorporates the second-order information to greatly speed up the convergence, while it avoids solving a regularized quadratic programming and only involves matrix-vector multiplications without explicitly forming the inverse Hessian matrix. (2) We establish a rigorous convergence analysis for HONOR, which shows that convergence is guaranteed even for non-convex problems, while it is typically challenging to analyze the convergence for non-convex problems. (3) We conduct empirical studies on large-scale data sets and results demonstrate that HONOR converges significantly faster than state-of-the-art algorithms.

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ICML Conference 2013 Conference Paper

A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems

Pinghua Gong
Changshui Zhang
Zhaosong Lu
Jianhua Huang
Jieping Ye

Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over the convex counterparts in several sparse learning settings. However, solving the non-convex optimization problems associated with non-convex penalties remains a big challenge. A commonly used approach is the Multi-Stage (MS) convex relaxation (or DC programming), which relaxes the original non-convex problem to a sequence of convex problems. This approach is usually not very practical for large-scale problems because its computational cost is a multiple of solving a single convex problem. In this paper, we propose a General Iterative Shrinkage and Thresholding (GIST) algorithm to solve the nonconvex optimization problem for a large class of non-convex penalties. The GIST algorithm iteratively solves a proximal operator problem, which in turn has a closed-form solution for many commonly used penalties. At each outer iteration of the algorithm, we use a line search initialized by the Barzilai-Borwein (BB) rule that allows finding an appropriate step size quickly. The paper also presents a detailed convergence analysis of the GIST algorithm. The efficiency of the proposed algorithm is demonstrated by extensive experiments on large-scale data sets.

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JMLR Journal 2013 Journal Article

Multi-Stage Multi-Task Feature Learning

Pinghua Gong
Jieping Ye
Changshui Zhang

Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applications including computer vision and biomedical informatics. Most of the existing multi-task sparse feature learning algorithms are formulated as a convex sparse regularization problem, which is usually suboptimal, due to its looseness for approximating an $\ell_0$-type regularizer. In this paper, we propose a non-convex formulation for multi-task sparse feature learning based on a novel non-convex regularizer. To solve the non-convex optimization problem, we propose a Multi-Stage Multi-Task Feature Learning (MSMTFL) algorithm; we also provide intuitive interpretations, detailed convergence and reproducibility analysis for the proposed algorithm. Moreover, we present a detailed theoretical analysis showing that MSMTFL achieves a better parameter estimation error bound than the convex formulation. Empirical studies on both synthetic and real-world data sets demonstrate the effectiveness of MSMTFL in comparison with the state of the art multi-task sparse feature learning algorithms. [abs] [ pdf ][ bib ] &copy JMLR 2013. ( edit, beta )

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AAAI Conference 2012 Conference Paper

Efficient Multi-Stage Conjugate Gradient for Trust Region Step

Pinghua Gong
Changshui Zhang

The trust region step problem, by solving a sphere constrained quadratic programming, plays a critical role in the trust region Newton method. In this paper, we propose an efficient Multi-Stage Conjugate Gradient (MSCG) algorithm to compute the trust region step in a multi-stage manner. Specifically, when the iterative solution is in the interior of the sphere, we perform the conjugate gradient procedure. Otherwise, we perform a gradient descent procedure which points to the inner of the sphere and can make the next iterative solution be a interior point. Subsequently, we proceed with the conjugate gradient procedure again. We repeat the above procedures until convergence. We also present a theoretical analysis which shows that the MSCG algorithm converges. Moreover, the proposed MSCG algorithm can generate a solution in any prescribed precision controlled by a tolerance parameter which is the only parameter we need. Experimental results on large-scale text data sets demonstrate our proposed MSCG algorithm has a faster convergence speed compared with the state-of-the-art algorithms.

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NeurIPS Conference 2012 Conference Paper

Multi-Stage Multi-Task Feature Learning

Pinghua Gong
Jieping Ye
Chang-shui Zhang

Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applications including computer vision and biomedical informatics. Most of the existing multi-task sparse feature learning algorithms are formulated as a convex sparse regularization problem, which is usually suboptimal, due to its looseness for approximating an $\ell_0$-type regularizer. In this paper, we propose a non-convex formulation for multi-task sparse feature learning based on a novel regularizer. To solve the non-convex optimization problem, we propose a Multi-Stage Multi-Task Feature Learning (MSMTFL) algorithm. Moreover, we present a detailed theoretical analysis showing that MSMTFL achieves a better parameter estimation error bound than the convex formulation. Empirical studies on both synthetic and real-world data sets demonstrate the effectiveness of MSMTFL in comparison with the state of the art multi-task sparse feature learning algorithms.

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