no code implementations • 20 Aug 2021 • Weicong Ding, Hanlin Tang, Jingshuo Feng, Lei Yuan, Sen yang, Guangxu Yang, Jie Zheng, Jing Wang, Qiang Su, Dong Zheng, Xuezhong Qiu, Yongqi Liu, Yuxuan Chen, Yang Liu, Chao Song, Dongying Kong, Kai Ren, Peng Jiang, Qiao Lian, Ji Liu
In this setting with multiple and constrained goals, this paper discovers that a probabilistic strategic parameter regime can achieve better value compared to the standard regime of finding a single deterministic parameter.
no code implementations • 23 Oct 2020 • Yunjie Zhang, Fei Tao, Xudong Liu, Runze Su, Xiaorong Mei, Weicong Ding, Zhichen Zhao, Lei Yuan, Ji Liu
In this paper, we proposed a novel end-to-end self-organizing framework for user behavior prediction.
2 code implementations • 6 Jul 2017 • Yifan Sun, Nikhil Rao, Weicong Ding
Evaluating these methods is also problematic, as rigorous quantitative evaluations in this space is limited, especially when compared with single-sense embeddings.
no code implementations • 4 May 2017 • Vatsal Shah, Nikhil Rao, Weicong Ding
While there has been recent research on incorporating explicit side information in the low-rank matrix factorization setting, often implicit information can be gleaned from the data, via higher-order interactions among entities.
2 code implementations • 2 Mar 2017 • Zijun Yao, Yifan Sun, Weicong Ding, Nikhil Rao, Hui Xiong
Word evolution refers to the changing meanings and associations of words throughout time, as a byproduct of human language evolution.
1 code implementation • 9 Nov 2016 • Weicong Ding, Christy Lin, Prakash Ishwar
Neural node embeddings have recently emerged as a powerful representation for supervised learning tasks involving graph-structured data.
no code implementations • 23 Aug 2015 • Weicong Ding, Prakash Ishwar, Venkatesh Saligrama
We develop necessary and sufficient conditions and a novel provably consistent and efficient algorithm for discovering topics (latent factors) from observations (documents) that are realized from a probabilistic mixture of shared latent factors that have certain properties.
no code implementations • 3 Apr 2015 • Weicong Ding, Prakash Ishwar, Venkatesh Saligrama
Our key algorithmic insight for estimation is to establish a statistical connection between M4 and topic models by viewing pairwise comparisons as words, and users as documents.
no code implementations • 11 Dec 2014 • Weicong Ding, Prakash Ishwar, Venkatesh Saligrama
We propose a topic modeling approach to the prediction of preferences in pairwise comparisons.
no code implementations • 2 Dec 2013 • Weicong Ding, Prakash Ishwar, Venkatesh Saligrama, W. Clem Karl
We propose a novel approach for designing kernels for support vector machines (SVMs) when the class label is linked to the observation through a latent state and the likelihood function of the observation given the state (the sensing model) is available.
no code implementations • 30 Oct 2013 • Weicong Ding, Prakash Ishwar, Mohammad H. Rohban, Venkatesh Saligrama
The simplicial condition and other stronger conditions that imply it have recently played a central role in developing polynomial time algorithms with provable asymptotic consistency and sample complexity guarantees for topic estimation in separable topic models.