154 citations · 359 across the 22 of their papers we have counts for
8 papers · 1 filter
SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering
Mingchen Li, Liqi Kang, Yi Xiong +4
Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functio…
MC-GEN:Multi-level Clustering for Private Synthetic Data Generation
Mingchen Li, Di Zhuang, J. Morris Chang
With the development of machine learning and data science, data sharing is very common between companies and research institutes to avoid data scarcity. However, sharing original d…
FedNest: Federated Bilevel, Minimax, and Compositional Optimization
Davoud Ataee Tarzanagh, Mingchen Li, Christos Thrampoulidis +1
Standard federated optimization methods successfully apply to stochastic problems with single-level structure. However, many contemporary ML problems -- including adversarial robus…
A Hierarchical N-Gram Framework for Zero-Shot Link Prediction
Mingchen Li, Junfan Chen, Samuel Mensah +3
Due to the incompleteness of knowledge graphs (KGs), zero-shot link prediction (ZSLP) which aims to predict unobserved relations in KGs has attracted recent interest from researche…
Semantic Structure based Query Graph Prediction for Question Answering over Knowledge Graph
Mingchen Li, Shihao Ji
Building query graphs from natural language questions is an important step in complex question answering over knowledge graph (Complex KGQA). In general, a question can be correctl…
Provable and Efficient Continual Representation Learning
Yingcong Li, Mingchen Li, M. Salman Asif +1
In continual learning (CL), the goal is to design models that can learn a sequence of tasks without catastrophic forgetting. While there is a rich set of techniques for CL, relativ…