Search Results for author: Junming Yin

Found 8 papers, 1 papers with code

Thompson Sampling for Bandit Learning in Matching Markets

1 code implementation26 Apr 2022 Fang Kong, Junming Yin, Shuai Li

The problem of two-sided matching markets has a wide range of real-world applications and has been extensively studied in the literature.

Multi-Armed Bandits

Deep Reinforcement Learning for Personalized Search Story Recommendation

no code implementations26 Jul 2019 Jason, Zhang, Junming Yin, Dongwon Lee, Linhong Zhu

In recent years, \emph{search story}, a combined display with other organic channels, has become a major source of user traffic on platforms such as e-commerce search platforms, news feed platforms and web and image search platforms.

Image Retrieval Imitation Learning +1

Multi-Label Annotation Aggregation in Crowdsourcing

no code implementations19 Jun 2017 Xuan Wei, Daniel Dajun Zeng, Junming Yin

As a means of human-based computation, crowdsourcing has been widely used to annotate large-scale unlabeled datasets.

Convex-constrained Sparse Additive Modeling and Its Extensions

no code implementations1 May 2017 Junming Yin, Yao-Liang Yu

Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression.

Additive models

Distributed Training of Deep Neural Networks with Theoretical Analysis: Under SSP Setting

no code implementations9 Dec 2015 Abhimanu Kumar, Pengtao Xie, Junming Yin, Eric P. Xing

We propose a distributed approach to train deep neural networks (DNNs), which has guaranteed convergence theoretically and great scalability empirically: close to 6 times faster on instance of ImageNet data set when run with 6 machines.

General Classification Image Classification

Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization

no code implementations13 Nov 2014 Linhong Zhu, Dong Guo, Junming Yin, Greg Ver Steeg, Aram Galstyan

We propose a scalable temporal latent space model for link prediction in dynamic social networks, where the goal is to predict links over time based on a sequence of previous graph snapshots.

Link Prediction

On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks

no code implementations NeurIPS 2012 Qirong Ho, Junming Yin, Eric P. Xing

A triangular motif is a vertex triple containing 2 or 3 edges, and the number of such motifs is $\Theta(\sum_{i}D_{i}^{2})$ (where $D_i$ is the degree of vertex $i$), which is much smaller than $N^2$ for low-maximum-degree networks.

Community Detection

Cannot find the paper you are looking for? You can Submit a new open access paper.