Kun Zhang

Causal Discovery Using Regression-Based Conditional Independence Tests

2017 · Proceedings of the AAAI Conference on Artificial Intelligence

Conditional independence (CI) testing is an important tool in causal discovery. Generally, by using CI tests, a set of Markov equivalence classes w.r.t. the observed data can be estimated by checking whether each pair of …

DRr-Net: Dynamic Re-Read Network for Sentence Semantic Matching

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks such as Natural Language Inference (NLI) and Paraphrase Identification (PI). Among all …

Joint Item Recommendation and Attribute Inference

2020

In many recommender systems, users and items are associated with attributes, and users show preferences to items. The attribute information describes users'(items') characteristics and has a wide range of applications, such as user profiling, item …

Invariant Action Effect Model for Reinforcement Learning

2022 · Proceedings of the AAAI Conference on Artificial Intelligence

Good representations can help RL agents perform concise modeling of their surroundings, and thus support effective decision-making in complex environments. Previous methods learn good representations by imposing extra constraints on dynamics. However, in the causal …

Scalable Causal Discovery with Score Matching

2023 · arXiv (Cornell University)

This paper demonstrates how to discover the whole causal graph from the second derivative of the log-likelihood in observational non-linear additive Gaussian noise models. Leveraging scalable machine learning approaches to approximate the score function $\nabla …

A Model Fusion Distributed Kalman Filter For Non-Gaussian Observation Noise

2023 · arXiv (Cornell University)

Wireless sensor networks (WSNs) represent a critical research domain within the Internet of Things (IoT) technology. The distributed Kalman filter (DKF) has garnered significant attention as an information fusion method for WSNs. However, effectively handling …

Collaborative Decision-Making Technique for Wireless Communication Means Based on Multi-Agent Reinforcement Learning

2024

Wireless communication is a pivotal domain within modern information and communication technology. With the proliferation of mobile devices and the Internet of Things, wireless communication methods have become ubiquitous across diverse industries. Challenges arise in …

Neural News Recommendation with Long- and Short-term User Representations

2019

Personalized news recommendation is important to help users find their interested news and improve reading experience. A key problem in news recommendation is learning accurate user representations to capture their interests. Users usually have both …

Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

Graph Convolutional Networks~(GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations and non-linear activation operations. Recently, in Collaborative Filtering~(CF) based Recommender Systems~(RS), by treating the user-item interaction …

Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization

2021

Neural graph based Collaborative Filtering (CF) models learn user and item embeddings based on the user-item bipartite graph structure, and have achieved state-of-the-art recommendation performance. In the ubiquitous implicit feedback based CF, users' unobserved behaviors …

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

2022 · IEEE Transactions on Knowledge and Data Engineering

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant …

A Review-aware Graph Contrastive Learning Framework for Recommendation

2022 · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Most modern recommender systems predict users' preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the auxiliary review information accompanied with user ratings, many of the existing …