no code implementations • 26 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.
no code implementations • 9 Oct 2020 • Yucheng Lin, Huiting Hong, Xiaoqing Yang, Xiaodi Yang, Pinghua Gong, Jieping Ye
Graph neural networks have become an important tool for modeling structured data.
no code implementations • 6 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.
1 code implementation • 23 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.
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.
no code implementations • 4 Jun 2014 • Pinghua Gong, Jieping Ye
Under the strongly convex condition, these variance-reduced stochastic gradient algorithms achieve a linear convergence rate.
4 code implementations • 18 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.
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.