Search Results for author: Pinghua Gong

Found 8 papers, 2 papers with code

Constrained Reinforcement Learning for Short Video Recommendation

no code implementations26 May 2022 Qingpeng Cai, Ruohan Zhan, Chi Zhang, Jie Zheng, Guangwei Ding, Pinghua Gong, Dong Zheng, Peng Jiang

In this paper, we formulate the problem of short video recommendation as a constrained Markov Decision Process (MDP), where platforms want to optimize the main goal of user watch time in long term, with the constraint of accommodating the auxiliary responses of user interactions such as sharing/downloading videos.

Recommendation Systems reinforcement-learning +1

A Survey on Machine Learning from Few Samples

no code implementations6 Sep 2020 Jiang Lu, Pinghua Gong, Jieping Ye, Jianwei Zhang, ChangShui Zhang

The capability of learning and generalizing from very few samples successfully is a noticeable demarcation separating artificial intelligence and human intelligence since humans can readily establish their cognition to novelty from just a single or a handful of examples whereas machine learning algorithms typically entail hundreds or thousands of supervised samples to guarantee generalization ability.

BIG-bench Machine Learning Meta-Learning

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

1 code implementation23 Feb 2018 Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, Zhenhui Li

Traditional demand prediction methods mostly rely on time series forecasting techniques, which fail to model the complex non-linear spatial and temporal relations.

Image Classification Time Series Forecasting +1

HONOR: Hybrid Optimization for NOn-convex Regularized problems

no code implementations NeurIPS 2015 Pinghua Gong, Jieping Ye

(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.

Sparse Learning

Linear Convergence of Variance-Reduced Stochastic Gradient without Strong Convexity

no code implementations4 Jun 2014 Pinghua Gong, Jieping Ye

Under the strongly convex condition, these variance-reduced stochastic gradient algorithms achieve a linear convergence rate.

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

4 code implementations18 Mar 2013 Pinghua Gong, Chang-Shui Zhang, Zhaosong Lu, Jianhua Huang, Jieping Ye

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.

Sparse Learning

Multi-Stage Multi-Task Feature Learning

no code implementations NeurIPS 2012 Pinghua Gong, Jieping Ye, Chang-Shui Zhang

In this paper, we propose a non-convex formulation for multi-task sparse feature learning based on a novel regularizer.

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