96 citations · 205 across the 9 of their papers we have counts for
9 papers
Multi-task Learning of Order-Consistent Causal Graphs
Xinshi Chen, Haoran Sun, Caleb Ellington +2
We consider the problem of discovering related Gaussian directed acyclic graphs (DAGs), where the involved graph structures share a consistent causal order and sparse unions of…
Learning to Stop While Learning to Predict
Xinshi Chen, Hanjun Dai, Yu Li +2
There is a recent surge of interest in designing deep architectures based on the update steps in traditional algorithms, or learning neural networks to improve and replace traditio…
Understanding Deep Architectures with Reasoning Layer
Xinshi Chen, Yufei Zhang, Christoph Reisinger +1
Recently, there has been a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks. In many cases, a reasoning task…
RNA Secondary Structure Prediction By Learning Unrolled Algorithms
Xinshi Chen, Yu Li, Ramzan Umarov +2
In this paper, we propose an end-to-end deep learning model, called E2Efold, for RNA secondary structure prediction which can effectively take into account the inherent constraints…
Efficient Probabilistic Logic Reasoning with Graph Neural Networks
Yuyu Zhang, Xinshi Chen, Yuan Yang +4
Markov Logic Networks (MLNs), which elegantly combine logic rules and probabilistic graphical models, can be used to address many knowledge graph problems. However, inference in ML…
Can Graph Neural Networks Help Logic Reasoning?
Yuyu Zhang, Xinshi Chen, Yuan Yang +4
Effectively combining logic reasoning and probabilistic inference has been a long-standing goal of machine learning: the former has the ability to generalize with small training da…
GLAD: Learning Sparse Graph Recovery
Harsh Shrivastava, Xinshi Chen, Binghong Chen +4
Recovering sparse conditional independence graphs from data is a fundamental problem in machine learning with wide applications. A popular formulation of the problem is an …
Particle Flow Bayes' Rule
Xinshi Chen, Hanjun Dai, Le Song
We present a particle flow realization of Bayes' rule, where an ODE-based neural operator is used to transport particles from a prior to its posterior after a new observation. We p…
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
Xinshi Chen, Shuang Li, Hui Li +3
There are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems. In this setting, an online user is the environment; neither…