Publications (13)
Necessary and Sufficient Conditions for Novel Word Detection in Separable Topic Models
Weicong Ding, Prakash Ishwar, Mohammad H. Rohban +1
The simplicial condition and other stronger conditions that imply it have recently played a central role in developing polynomial time algorithms with provable asymptotic consisten…
PASTO: Strategic Parameter Optimization in Recommendation Systems -- Probabilistic is Better than Deterministic
Weicong Ding, Hanlin Tang, Jingshuo Feng +17
Real-world recommendation systems often consist of two phases. In the first phase, multiple predictive models produce the probability of different immediate user actions. In the se…
Dynamic Word Embeddings for Evolving Semantic Discovery
Zijun Yao, Yifan Sun, Weicong Ding +2
Word evolution refers to the changing meanings and associations of words throughout time, as a byproduct of human language evolution. By studying word evolution, we can infer socia…
Short Video-based Advertisements Evaluation System: Self-Organizing Learning Approach
Yunjie Zhang, Fei Tao, Xudong Liu +6
With the rising of short video apps, such as TikTok, Snapchat and Kwai, advertisement in short-term user-generated videos (UGVs) has become a trending form of advertising. Predicti…
Matrix Completion via Factorizing Polynomials
Vatsal Shah, Nikhil Rao, Weicong Ding
Predicting unobserved entries of a partially observed matrix has found wide applicability in several areas, such as recommender systems, computational biology, and computer vision.…
A Simple Approach to Learn Polysemous Word Embeddings
Yifan Sun, Nikhil Rao, Weicong Ding
Many NLP applications require disambiguating polysemous words. Existing methods that learn polysemous word vector representations involve first detecting various senses and optimiz…